File size: 117,341 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 | {
"paper_id": "2022",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T01:11:44.995149Z"
},
"title": "Pipelines for Social Bias Testing of Large Language Models",
"authors": [
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Bocconi University",
"location": {
"addrLine": "Via Sarfatti 25",
"settlement": "Milan",
"country": "Italy"
}
},
"email": "debora.nozza@unibocconi.it"
},
{
"first": "Federico",
"middle": [],
"last": "Bianchi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Bocconi University",
"location": {
"addrLine": "Via Sarfatti 25",
"settlement": "Milan",
"country": "Italy"
}
},
"email": "f.bianchi@unibocconi.it"
},
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Bocconi University",
"location": {
"addrLine": "Via Sarfatti 25",
"settlement": "Milan",
"country": "Italy"
}
},
"email": "dirk.hovy@unibocconi.it"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, systematic ways of integrating social bias tests into development pipelines are still lacking. This short paper suggests how to use these verification techniques in development pipelines. We take inspiration from software testing and suggest addressing social bias evaluation as software testing. We hope to open a discussion on the best methodologies to handle social bias testing in language models.",
"pdf_parse": {
"paper_id": "2022",
"_pdf_hash": "",
"abstract": [
{
"text": "The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while research has shown how biased and harmful these models are, systematic ways of integrating social bias tests into development pipelines are still lacking. This short paper suggests how to use these verification techniques in development pipelines. We take inspiration from software testing and suggest addressing social bias evaluation as software testing. We hope to open a discussion on the best methodologies to handle social bias testing in language models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Current language models are now primarily deployed on large infrastructures (e.g., HuggingFace repository 1 ) and used by many practitioners and researchers with few lines of code. This releasing mechanism has brought tremendous value to the community as researchers everywhere can access models, download them on their laptops, and run experiments. However, these models are quickly adopted without complete understanding their possible limitations .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Recent literature is now rich of papers that demonstrate how social bias is embedded in large language models and propose many different verification and validation datasets (e.g., May et al., 2019; Nozza et al., 2021; Nadeem et al., 2021, inter alia) . Researchers and practitioners can use all these contributions to understand if a model is safe to use or not. We will refer to these works and the datasets used as verification as social bias tests from this point on.",
"cite_spans": [
{
"start": 181,
"end": 198,
"text": "May et al., 2019;",
"ref_id": "BIBREF26"
},
{
"start": 199,
"end": 218,
"text": "Nozza et al., 2021;",
"ref_id": "BIBREF29"
},
{
"start": 219,
"end": 251,
"text": "Nadeem et al., 2021, inter alia)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "This literature often misses the long-term goal. What is the point of having so many social bias tests that effectively capture different aspects of the problem if we do not find a systematic way of using them? Indeed, this work is also inspired 1 https://huggingface.co/ by the recent approaches and methodologies defined to provide more comprehensive evaluations of models (Ribeiro et al., 2020; Chia et al., 2022) .",
"cite_spans": [
{
"start": 375,
"end": 397,
"text": "(Ribeiro et al., 2020;",
"ref_id": "BIBREF33"
},
{
"start": 398,
"end": 416,
"text": "Chia et al., 2022)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Indeed, other computer science fields have developed insights into how to handle testing. Software development has long been wrestling with the need for good evaluation practices for source code. For example, Continuous Integration and Continuous Deployment (CI/CD) is a general methodology in software development. It assumes frequent testing to ensure that the product under development passes specific qualitative tests that guarantee it is working. In this direction, frequent testing of language models can be part of the solution.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main contribution of this short paper is first to identify the main recurring themes and the primary methodologies of social bias literature. We then suggest a more practical and developmental direction: all these methods can be used the same way as tests in software testing pipelines. Unstable/unsafe software should not go into production, which is also true for language models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We are aware that a single social bias test cannot provide a complete picture of the problems and that we cannot treat a model that passes the tests as entirely safe. Nonetheless, we believe that some frequent tests are better than no tests. As a community, we need to come together and work closely to stress test these models even during the development phase.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Contributions Our contribution is twofold: we first give an overview of the literature on social bias tests and explore the main themes and methods. We then suggest that this literature can be used in practical contexts to frequently evaluate language models to understand better how the tools we use can be harmful. With this work, we hope to start a discussion on the best methodologies to handle social bias testing in language models as we believe this is a fundamental step to sustain the future and correct usage of these technologies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "An overview of bias in NLP has been presented in several work (Blodgett et al., 2020; Shah et al., 2020; Hovy and Prabhumoye, 2021; Sheng et al., 2021; Stanczak and Augenstein, 2021) . Here, we focus on the approaches proposed for contextual embeddings. We illustrate the main themes that have driven the developed of social bias tests. The categories we are going to describe are not mutually exclusive, however they showcase in a coherent manner what has been done in the literature.",
"cite_spans": [
{
"start": 62,
"end": 85,
"text": "(Blodgett et al., 2020;",
"ref_id": "BIBREF3"
},
{
"start": 86,
"end": 104,
"text": "Shah et al., 2020;",
"ref_id": "BIBREF35"
},
{
"start": 105,
"end": 131,
"text": "Hovy and Prabhumoye, 2021;",
"ref_id": "BIBREF17"
},
{
"start": 132,
"end": 151,
"text": "Sheng et al., 2021;",
"ref_id": "BIBREF36"
},
{
"start": 152,
"end": 182,
"text": "Stanczak and Augenstein, 2021)",
"ref_id": "BIBREF38"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Existing Social Bias tests",
"sec_num": "2"
},
{
"text": "Several studies have been conducted to analyse and determine the level of bias in static word embeddings in binary and multi-class scenarios (Bolukbasi et al., 2016; Caliskan et al., 2017; Garg et al., 2018; Swinger et al., 2019; Manzini et al., 2019; Lauscher and Glava\u0161, 2019; Gonen and Goldberg, 2019) . Several works applied these bias evaluations to contextualized models by extracting static word embeddings for them (Basta et al., 2019; Wolfe and Caliskan, 2021) .",
"cite_spans": [
{
"start": 141,
"end": 165,
"text": "(Bolukbasi et al., 2016;",
"ref_id": "BIBREF6"
},
{
"start": 166,
"end": 188,
"text": "Caliskan et al., 2017;",
"ref_id": "BIBREF7"
},
{
"start": 189,
"end": 207,
"text": "Garg et al., 2018;",
"ref_id": "BIBREF12"
},
{
"start": 208,
"end": 229,
"text": "Swinger et al., 2019;",
"ref_id": "BIBREF39"
},
{
"start": 230,
"end": 251,
"text": "Manzini et al., 2019;",
"ref_id": "BIBREF25"
},
{
"start": 252,
"end": 278,
"text": "Lauscher and Glava\u0161, 2019;",
"ref_id": "BIBREF22"
},
{
"start": 279,
"end": 304,
"text": "Gonen and Goldberg, 2019)",
"ref_id": "BIBREF16"
},
{
"start": 423,
"end": 443,
"text": "(Basta et al., 2019;",
"ref_id": "BIBREF1"
},
{
"start": 444,
"end": 469,
"text": "Wolfe and Caliskan, 2021)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word List-based",
"sec_num": "2.1"
},
{
"text": "Inspired by gender bias metrics for word embeddings, May et al. (2019) proposed the Sentence Encoder Association Test (SEAT), a template-based test founded on the Word Embedding Association Test (WEAT) (Caliskan et al., 2017) . Afterward, Liang et al. (2020) used SEAT for measuring bias, also considering the religious dimension.",
"cite_spans": [
{
"start": 53,
"end": 70,
"text": "May et al. (2019)",
"ref_id": "BIBREF26"
},
{
"start": 202,
"end": 225,
"text": "(Caliskan et al., 2017)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word List-based",
"sec_num": "2.1"
},
{
"text": "Template-based approaches exploit the fact that BERT-like models are trained using a masked language modeling objective. I.e., given a sentence with omitted tokens indicated as [MASK] , they predict the masked tokens. The predictions for these [MASK] tokens may provide us with some insight into the bias embedded in the actual representations. We can generate templates in two different ways. First, by accounting for certain targets (e.g., gendered words) and attributes (e.g. career-related words) (Kurita et al., 2019; Dev et al., 2020) . This enable, for example, to compute the association between the target male gender and the attribute programmer, by feeding \"[MASK] is a programmer\" to BERT, and compute the probability assigned to the sentence \"he is a programmer\". Another option is to create templates coupling protected group targets with neutral predicates (e.g., \"works as\", \"is known for\"). For example, we can ask BERT to complete \"the woman is known for [MASK]\" or \"the girl worked as [MASK] .\" Then, it is possible to exploit lexicons (Nozza et al., 2021 (Nozza et al., , 2022 , or hate speech (Ousidhoum et al., 2021; Sheng et al., 2019) and sentiment classifiers Hutchinson et al. (2020) ; Huang et al. (2020) to obtain a social bias score from the template-based generated text. Ideally, using a classifier lets us test the data more easily and accurately than lexicons.",
"cite_spans": [
{
"start": 177,
"end": 183,
"text": "[MASK]",
"ref_id": null
},
{
"start": 244,
"end": 250,
"text": "[MASK]",
"ref_id": null
},
{
"start": 501,
"end": 522,
"text": "(Kurita et al., 2019;",
"ref_id": "BIBREF21"
},
{
"start": 523,
"end": 540,
"text": "Dev et al., 2020)",
"ref_id": "BIBREF10"
},
{
"start": 1004,
"end": 1010,
"text": "[MASK]",
"ref_id": null
},
{
"start": 1055,
"end": 1074,
"text": "(Nozza et al., 2021",
"ref_id": "BIBREF29"
},
{
"start": 1075,
"end": 1096,
"text": "(Nozza et al., , 2022",
"ref_id": "BIBREF30"
},
{
"start": 1114,
"end": 1138,
"text": "(Ousidhoum et al., 2021;",
"ref_id": "BIBREF32"
},
{
"start": 1139,
"end": 1158,
"text": "Sheng et al., 2019)",
"ref_id": "BIBREF37"
},
{
"start": 1185,
"end": 1209,
"text": "Hutchinson et al. (2020)",
"ref_id": "BIBREF19"
},
{
"start": 1212,
"end": 1231,
"text": "Huang et al. (2020)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Template-based",
"sec_num": "2.2"
},
{
"text": "The same approach can be applied to natural language generation models (Sheng et al., 2019; Huang et al., 2020) . The models are not fed with a masked token but are asked to complete the template. So, instead of a single word, they return a set of words.",
"cite_spans": [
{
"start": 71,
"end": 91,
"text": "(Sheng et al., 2019;",
"ref_id": "BIBREF37"
},
{
"start": 92,
"end": 111,
"text": "Huang et al., 2020)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Template-based",
"sec_num": "2.2"
},
{
"text": "An interesting case has been proposed by Choenni et al.. They look into what kinds of stereotyped information are collected by LLMs exploiting a dataset comprising stereotypical attributes for various social groups. The dataset was created by feeding search engines queries that already imply a stereotype about a specific social group (e.g., 'Why are Asian parents so'). Then, the authors count how many of the stereotypes found by the search engines are also encoded in the LLMs through masked language modeling.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Template-based",
"sec_num": "2.2"
},
{
"text": "Few works have collected datasets to compute bias scores. Nadeem et al. (2021) presented StereoSet, a crowdsourced English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. Nangia et al. (2020) introduced CrowS-Pairs, a crowdsourced benchmark comprising 1508 examples that cover stereotypes dealing with nine types of bias. Both Nadeem et al. (2021) ; Nangia et al. (2020) proposed a metric to measure for how many examples the model prefers stereotyped sentences over less stereotyped sentences.",
"cite_spans": [
{
"start": 58,
"end": 78,
"text": "Nadeem et al. (2021)",
"ref_id": "BIBREF27"
},
{
"start": 220,
"end": 240,
"text": "Nangia et al. (2020)",
"ref_id": "BIBREF28"
},
{
"start": 376,
"end": 396,
"text": "Nadeem et al. (2021)",
"ref_id": "BIBREF27"
},
{
"start": 399,
"end": 419,
"text": "Nangia et al. (2020)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Crowdsourced-based",
"sec_num": "2.3"
},
{
"text": "These messages are then split in half (a prompt and a continuation) and are used to study, for example, whether a model generates toxic continuations from a non-toxic prompt.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Crowdsourced-based",
"sec_num": "2.3"
},
{
"text": "While many social bias tests have been provided in the literature, they differ in methodology, covered languages, and protected groups. Most works are on English. Only (Nozza et al., 2021; Ousidhoum et al., 2021) considered languages beyond English. The majority of work focused on gender bias, and only a few investigated an extensive range of targets (Nangia et al., 2020; Nadeem et al., 2021; Ousidhoum et al., 2021; Barikeri et al., 2021) . We also found that Hutchinson et al. (2020) ; Huang et al. (2020) did not provide data or code publicly. Blodgett et al. 2021presented a critical review of some social bias tests and found significant issues with noise, unnaturalness, and reliability of the some work (Nangia et al., 2020; Nadeem et al., 2021) . Finally, it is important to highlight that social biases are different depending on the cultural and historical context of application of the language model. This brief analysis demonstrates that no existing social bias test is universal. While we may fill this research gap in the future, for now, we suggest using more than one test has to be used to measure bias.",
"cite_spans": [
{
"start": 168,
"end": 188,
"text": "(Nozza et al., 2021;",
"ref_id": "BIBREF29"
},
{
"start": 189,
"end": 212,
"text": "Ousidhoum et al., 2021)",
"ref_id": "BIBREF32"
},
{
"start": 353,
"end": 374,
"text": "(Nangia et al., 2020;",
"ref_id": "BIBREF28"
},
{
"start": 375,
"end": 395,
"text": "Nadeem et al., 2021;",
"ref_id": "BIBREF27"
},
{
"start": 396,
"end": 419,
"text": "Ousidhoum et al., 2021;",
"ref_id": "BIBREF32"
},
{
"start": 420,
"end": 442,
"text": "Barikeri et al., 2021)",
"ref_id": "BIBREF0"
},
{
"start": 464,
"end": 488,
"text": "Hutchinson et al. (2020)",
"ref_id": "BIBREF19"
},
{
"start": 713,
"end": 734,
"text": "(Nangia et al., 2020;",
"ref_id": "BIBREF28"
},
{
"start": 735,
"end": 755,
"text": "Nadeem et al., 2021)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "2.5"
},
{
"text": "We describe the different modalities that can be used to integrate social bias tests into development pipelines.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Integration",
"sec_num": "3"
},
{
"text": "Software testing is at the heart of software development. Without good evaluation, software easily breaks in production, causing economic damage to companies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Continuous Social Bias Verification",
"sec_num": "3.1"
},
{
"text": "Most of the checks currently run to test language models are structural. For example, does it produce outputs correctly? Once fine-tuned, are the results we get in a sensible range? We suggest that tests should cover social biases.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Continuous Social Bias Verification",
"sec_num": "3.1"
},
{
"text": "We take inspiration from software testing and suggest testing methodologies for language models. In a CI/CD (continuous integration and continuous development) setting, code is continuously pushed into the repository and tested to ensure the model is stable. Software is deployed if and only if tests are correctly passed. We believe that we should replicate this pipeline in the development of language models. Every time a new model is released, we can run tests to verify if and how the model is hurtful.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Continuous Social Bias Verification",
"sec_num": "3.1"
},
{
"text": "Note that this is indeed a real problem. Many pipelines are now based on HuggingFace APIs that directly download the model from the HuggingFace Hub. Users might not know what happens on the backend: what happens when a model is updated, and the user downloads it thinking it is the same as the older version? We are not sure how many users keep track of commits and changelogs, and this might create a misunderstanding about which model is being used and with which training setup.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Continuous Social Bias Verification",
"sec_num": "3.1"
},
{
"text": "Publishers may help maintain the fairness of the research ecosystem by establishing a badging mechanism. This approach would increase the likelihood that an LLM will be tested in advance for social biases and that end-users will pay attention to this issue.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Badge System",
"sec_num": "3.2"
},
{
"text": "Here, we propose a badging system based on the ACM one 2 and the one proposed for the NAACL 2022 reproducibility track 3 . We identified three possible badges: Social Bias Evaluated, Social Bias Available, and Results Validated.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Badge System",
"sec_num": "3.2"
},
{
"text": "This badge is given to LLMs who have successfully run the social bias tests. This badge does not require the scores to be made publicly available.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Social Bias Evaluated",
"sec_num": null
},
{
"text": "LLMs that made the results of social bias tests retrievable. We propose to design one badge for each implemented social bias test and to show it along with the associated score. We discourage using badges as binary (i.e., test passed or test failed) for these particular cases. Considering the problem as binary might imply that a passing model is entirely free of bias, even if this is not the case.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Social Bias Available This badge is given to",
"sec_num": null
},
{
"text": "Results Validated This badge is given to LLMs in which the social bias test results were successfully attained by a person or team other than the author. A model can be developed and trained on a server and pushed online. Then we can use an automation tool (e.g., Github Actions) to start an evaluation engine that will eventually generate the predictions for the models. Once scored, the model can be released online with badges identifying possible issues that one might encounter with the model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Social Bias Available This badge is given to",
"sec_num": null
},
{
"text": "Badging is also a standard and straightforward system to showcase software validity in an online repository. These badges are often used to show information about the number of downloads, the test coverage, the quality of the documentation and allow users to understand the quality of what they are using with a quick look. Figure 1 shows a possible integration of testing for harms in development pipelines. We can develop the models on a local server and push this model online after training is finished (with Git LSF, for example). Pushing should automatically start an evaluation pipeline (something close to Github Actions) that starts an evaluation engine: this engine should load the models and run the social bias tests. Once the results are collected, and the metrics have been scored, the model can finally appear on online repositories with badges that identify if and how the test have been run with the respective scores.",
"cite_spans": [],
"ref_spans": [
{
"start": 324,
"end": 332,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Social Bias Available This badge is given to",
"sec_num": null
},
{
"text": "An open question is if the test should be available to the developer of the models. On the one hand, releasing the tests makes it easier for everyone to evaluate their models internally before release. On the other hand, this makes it easier to \"train on test\" and hack the system to obtain better scores.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Limits of this Integration",
"sec_num": "3.3"
},
{
"text": "Hiding the test sets from the developer is closer to standard Quality And Assurance developers in companies that are meant to test the interfaces and the code that the developer has built. This approach is also in line with challenges that do not share test data and in which models are submitted using docker containers that are then internally evaluated and scored. As Goodhart's law states, \"When a measure becomes a target, it ceases to be a good measure\". Thus we should be aware that social bias tests cannot be the panacea for language models problems. We cannot rely only on a test to assess the validity of a model. 4 Another point in discussion is that the pipelines we have designed are meant to evaluate intrinsic bias in language models. Unfortunately, this does not consider the verification of bias in downstream application: this extrinsic bias has been found to be poorly correlated with the original bias of language models (Goldfarb-Tarrant et al., 2021) . However, we want to point out the an additional set of application-specific tests could be used to evaluate the models adapted for these tasks: for example, researchers could use hate speech check tests (Dixon et al., 2018; Nozza et al., 2019; R\u00f6ttger et al., 2021) to verify social biases in hate speech detection models.",
"cite_spans": [
{
"start": 625,
"end": 626,
"text": "4",
"ref_id": null
},
{
"start": 942,
"end": 973,
"text": "(Goldfarb-Tarrant et al., 2021)",
"ref_id": "BIBREF15"
},
{
"start": 1179,
"end": 1199,
"text": "(Dixon et al., 2018;",
"ref_id": "BIBREF11"
},
{
"start": 1200,
"end": 1219,
"text": "Nozza et al., 2019;",
"ref_id": "BIBREF31"
},
{
"start": 1220,
"end": 1241,
"text": "R\u00f6ttger et al., 2021)",
"ref_id": "BIBREF34"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Limits of this Integration",
"sec_num": "3.3"
},
{
"text": "This paper proposes to use social bias tests in model development pipelines. We believe that our work can be helpful to make the development of these models fairer and easier to sustain from an ethical point of view. Future work is needed to answer several questions about this system. For example, who creates the tests and how can we make sure that these tests can be trusted? It becomes critical to involve marginalized communities to develop more sustainable and effective social bias tests.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "4"
},
{
"text": ".4 Social Media-basedBarikeri et al. (2021) propose a bias evaluation framework for conversational LLMs using REDDIT-BIAS, an English conversational data set grounded in real-world human conversations from Reddit. The authors propose a perplexity-based bias measure meant to quantify the amount of bias in generative language models along several bias dimensions. Gehman et al. (2020) focus on collecting prompts from the OpenWebText Corpus(Gokaslan and Cohen, 2019) and annotating them with the Perspective API to evaluate the toxicity of the messages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://www.acm.org/ publications/policies/ artifact-review-and-badging-current 3 https://2022.naacl.org/blog/ reproducibility-track/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Albeit, this comment is true for any measure we use in the field.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This project has partially received funding from the European Research Council ( ",
"cite_spans": [],
"ref_spans": [
{
"start": 79,
"end": 80,
"text": "(",
"ref_id": null
}
],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
},
{
"text": "We understand that providing social bias tests as a quantifiable indicator for bias carries a significant risk. A low score on a social bias test might be used to assert that a model is fully devoid of bias. As Nangia et al. (2020), we strongly advise against this. Tests can be an indication of issues. Conversely,the absence of a high score does not necessarily entail the absence of bias. Neither do replace a thorough investigation of the data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Ethical Statements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models",
"authors": [
{
"first": "Soumya",
"middle": [],
"last": "Barikeri",
"suffix": ""
},
{
"first": "Anne",
"middle": [],
"last": "Lauscher",
"suffix": ""
},
{
"first": "Ivan",
"middle": [],
"last": "Vuli\u0107",
"suffix": ""
},
{
"first": "Goran",
"middle": [],
"last": "Glava\u0161",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "1941--1955",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.151"
]
},
"num": null,
"urls": [],
"raw_text": "Soumya Barikeri, Anne Lauscher, Ivan Vuli\u0107, and Goran Glava\u0161. 2021. RedditBias: A real-world resource for bias evaluation and debiasing of conversational lan- guage models. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1941-1955, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Evaluating the underlying gender bias in contextualized word embeddings",
"authors": [
{
"first": "Christine",
"middle": [],
"last": "Basta",
"suffix": ""
},
{
"first": "Marta",
"middle": [
"R"
],
"last": "Costa-Juss\u00e0",
"suffix": ""
},
{
"first": "Noe",
"middle": [],
"last": "Casas",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "33--39",
"other_ids": {
"DOI": [
"10.18653/v1/W19-3805"
]
},
"num": null,
"urls": [],
"raw_text": "Christine Basta, Marta R. Costa-juss\u00e0, and Noe Casas. 2019. Evaluating the underlying gender bias in con- textualized word embeddings. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 33-39, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "On the gap between adoption and understanding in NLP",
"authors": [
{
"first": "Federico",
"middle": [],
"last": "Bianchi",
"suffix": ""
},
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2021,
"venue": "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
"volume": "",
"issue": "",
"pages": "3895--3901",
"other_ids": {
"DOI": [
"10.18653/v1/2021.findings-acl.340"
]
},
"num": null,
"urls": [],
"raw_text": "Federico Bianchi and Dirk Hovy. 2021. On the gap be- tween adoption and understanding in NLP. In Find- ings of the Association for Computational Linguis- tics: ACL-IJCNLP 2021, pages 3895-3901, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Language (technology) is power: A critical survey of \"bias\" in NLP",
"authors": [
{
"first": "",
"middle": [],
"last": "Su Lin",
"suffix": ""
},
{
"first": "Solon",
"middle": [],
"last": "Blodgett",
"suffix": ""
},
{
"first": "Hal",
"middle": [],
"last": "Barocas",
"suffix": ""
},
{
"first": "Iii",
"middle": [],
"last": "Daum\u00e9",
"suffix": ""
},
{
"first": "Hanna",
"middle": [],
"last": "Wallach",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "5454--5476",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.485"
]
},
"num": null,
"urls": [],
"raw_text": "Su Lin Blodgett, Solon Barocas, Hal Daum\u00e9 III, and Hanna Wallach. 2020. Language (technology) is power: A critical survey of \"bias\" in NLP. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 5454- 5476, Online. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets",
"authors": [
{
"first": "",
"middle": [],
"last": "Su Lin",
"suffix": ""
},
{
"first": "Gilsinia",
"middle": [],
"last": "Blodgett",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Lopez",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Olteanu",
"suffix": ""
},
{
"first": "Hanna",
"middle": [],
"last": "Sim",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Wallach",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.81"
]
},
"num": null,
"urls": [],
"raw_text": "Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 2021. Stereotyping Norwegian salmon: An inventory of pitfalls in fair- ness benchmark datasets. In Proceedings of the 59th",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"authors": [],
"year": null,
"venue": "",
"volume": "1",
"issue": "",
"pages": "1004--1015",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 1004-1015, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Man is to computer programmer as woman is to homemaker? debiasing word embeddings",
"authors": [
{
"first": "Tolga",
"middle": [],
"last": "Bolukbasi",
"suffix": ""
},
{
"first": "Kai-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "James",
"middle": [
"Y"
],
"last": "Zou",
"suffix": ""
},
{
"first": "Venkatesh",
"middle": [],
"last": "Saligrama",
"suffix": ""
},
{
"first": "Adam Tauman",
"middle": [],
"last": "Kalai",
"suffix": ""
}
],
"year": 2016,
"venue": "Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "4349--4357",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Ad- vances in Neural Information Processing Systems 29: Annual Conference on Neural Information Process- ing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 4349-4357.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Semantics derived automatically from language corpora contain human-like biases",
"authors": [
{
"first": "Aylin",
"middle": [],
"last": "Caliskan",
"suffix": ""
},
{
"first": "Joanna",
"middle": [
"J"
],
"last": "Bryson",
"suffix": ""
},
{
"first": "Arvind",
"middle": [],
"last": "Narayanan",
"suffix": ""
}
],
"year": 2017,
"venue": "Science",
"volume": "356",
"issue": "6334",
"pages": "183--186",
"other_ids": {
"DOI": [
"10.1126/science.aal4230"
]
},
"num": null,
"urls": [],
"raw_text": "Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183-186.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Beyond ndcg: behavioral testing of recommender systems with reclist",
"authors": [
{
"first": "Patrick John",
"middle": [],
"last": "Chia",
"suffix": ""
},
{
"first": "Jacopo",
"middle": [],
"last": "Tagliabue",
"suffix": ""
},
{
"first": "Federico",
"middle": [],
"last": "Bianchi",
"suffix": ""
},
{
"first": "Chloe",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Ko",
"suffix": ""
}
],
"year": 2022,
"venue": "Companion Proceedings of the Web Conference",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Chloe He, and Brian Ko. 2022. Beyond ndcg: behav- ioral testing of recommender systems with reclist. In Companion Proceedings of the Web Conference.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?",
"authors": [
{
"first": "Rochelle",
"middle": [],
"last": "Choenni",
"suffix": ""
},
{
"first": "Ekaterina",
"middle": [],
"last": "Shutova",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Van Rooij",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1477--1491",
"other_ids": {
"DOI": [
"10.18653/v1/2021.emnlp-main.111"
]
},
"num": null,
"urls": [],
"raw_text": "Rochelle Choenni, Ekaterina Shutova, and Robert van Rooij. 2021. Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? In Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 1477-1491, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "On measuring and mitigating biased inferences of word embeddings",
"authors": [
{
"first": "Sunipa",
"middle": [],
"last": "Dev",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jeff",
"middle": [
"M"
],
"last": "Phillips",
"suffix": ""
},
{
"first": "Vivek",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 2020,
"venue": "The Thirty-Second Innovative Applications of Artificial Intelligence Conference",
"volume": "2020",
"issue": "",
"pages": "7659--7666",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sunipa Dev, Tao Li, Jeff M. Phillips, and Vivek Sriku- mar. 2020. On measuring and mitigating biased in- ferences of word embeddings. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, pages 7659-7666. AAAI Press.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Measuring and mitigating unintended bias in text classification",
"authors": [
{
"first": "Lucas",
"middle": [],
"last": "Dixon",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Sorensen",
"suffix": ""
},
{
"first": "Nithum",
"middle": [],
"last": "Thain",
"suffix": ""
},
{
"first": "Lucy",
"middle": [],
"last": "Vasserman",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES '18",
"volume": "",
"issue": "",
"pages": "67--73",
"other_ids": {
"DOI": [
"10.1145/3278721.3278729"
]
},
"num": null,
"urls": [],
"raw_text": "Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018. Measuring and mitigat- ing unintended bias in text classification. In Proceed- ings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES '18, page 67-73, New York, NY, USA. Association for Computing Machinery.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Word embeddings quantify 100 years of gender and ethnic stereotypes",
"authors": [
{
"first": "Nikhil",
"middle": [],
"last": "Garg",
"suffix": ""
},
{
"first": "Londa",
"middle": [],
"last": "Schiebinger",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Jurafsky",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Zou",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the National Academy of Sciences",
"volume": "115",
"issue": "16",
"pages": "3635--3644",
"other_ids": {
"DOI": [
"10.1073/pnas.1720347115"
]
},
"num": null,
"urls": [],
"raw_text": "Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635- E3644.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "RealToxi-cityPrompts: Evaluating neural toxic degeneration in language models",
"authors": [
{
"first": "Suchin",
"middle": [],
"last": "Samuel Gehman",
"suffix": ""
},
{
"first": "Maarten",
"middle": [],
"last": "Gururangan",
"suffix": ""
},
{
"first": "Yejin",
"middle": [],
"last": "Sap",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Choi",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Smith",
"suffix": ""
}
],
"year": 2020,
"venue": "Findings of the Association for Computational Linguistics: EMNLP 2020",
"volume": "",
"issue": "",
"pages": "3356--3369",
"other_ids": {
"DOI": [
"10.18653/v1/2020.findings-emnlp.301"
]
},
"num": null,
"urls": [],
"raw_text": "Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020. RealToxi- cityPrompts: Evaluating neural toxic degeneration in language models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356-3369, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Openwebtext corpus",
"authors": [
{
"first": "Aaron",
"middle": [],
"last": "Gokaslan",
"suffix": ""
},
{
"first": "Vanya",
"middle": [],
"last": "Cohen",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://Skylion007.github.io/ OpenWebTextCorpus.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Intrinsic bias metrics do not correlate with application bias",
"authors": [
{
"first": "Seraphina",
"middle": [],
"last": "Goldfarb-Tarrant",
"suffix": ""
},
{
"first": "Rebecca",
"middle": [],
"last": "Marchant",
"suffix": ""
},
{
"first": "Ricardo",
"middle": [
"Mu\u00f1oz"
],
"last": "S\u00e1nchez",
"suffix": ""
},
{
"first": "Mugdha",
"middle": [],
"last": "Pandya",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Lopez",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "1926--1940",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.150"
]
},
"num": null,
"urls": [],
"raw_text": "Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ri- cardo Mu\u00f1oz S\u00e1nchez, Mugdha Pandya, and Adam Lopez. 2021. Intrinsic bias metrics do not correlate with application bias. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 1926-1940, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them",
"authors": [
{
"first": "Hila",
"middle": [],
"last": "Gonen",
"suffix": ""
},
{
"first": "Yoav",
"middle": [],
"last": "Goldberg",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "609--614",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1061"
]
},
"num": null,
"urls": [],
"raw_text": "Hila Gonen and Yoav Goldberg. 2019. Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 609-614, Minneapolis, Minnesota. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Five sources of bias in natural language processing. Language and Linguistics Compass",
"authors": [
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": ""
},
{
"first": "Shrimai",
"middle": [],
"last": "Prabhumoye",
"suffix": ""
}
],
"year": 2021,
"venue": "",
"volume": "15",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dirk Hovy and Shrimai Prabhumoye. 2021. Five sources of bias in natural language processing. Lan- guage and Linguistics Compass, 15(8):e12432.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Reducing sentiment bias in language models via counterfactual evaluation",
"authors": [
{
"first": "Po-Sen",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Huan",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Ray",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Stanforth",
"suffix": ""
},
{
"first": "Johannes",
"middle": [],
"last": "Welbl",
"suffix": ""
},
{
"first": "Jack",
"middle": [],
"last": "Rae",
"suffix": ""
},
{
"first": "Vishal",
"middle": [],
"last": "Maini",
"suffix": ""
},
{
"first": "Dani",
"middle": [],
"last": "Yogatama",
"suffix": ""
},
{
"first": "Pushmeet",
"middle": [],
"last": "Kohli",
"suffix": ""
}
],
"year": 2020,
"venue": "Findings of the Association for Computational Linguistics: EMNLP 2020",
"volume": "",
"issue": "",
"pages": "65--83",
"other_ids": {
"DOI": [
"10.18653/v1/2020.findings-emnlp.7"
]
},
"num": null,
"urls": [],
"raw_text": "Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stan- forth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 2020. Reducing sen- timent bias in language models via counterfactual evaluation. In Findings of the Association for Com- putational Linguistics: EMNLP 2020, pages 65-83, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Social biases in NLP models as barriers for persons with disabilities",
"authors": [
{
"first": "Ben",
"middle": [],
"last": "Hutchinson",
"suffix": ""
},
{
"first": "Vinodkumar",
"middle": [],
"last": "Prabhakaran",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Denton",
"suffix": ""
},
{
"first": "Kellie",
"middle": [],
"last": "Webster",
"suffix": ""
},
{
"first": "Yu",
"middle": [],
"last": "Zhong",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Denuyl",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.487"
]
},
"num": null,
"urls": [],
"raw_text": "Ben Hutchinson, Vinodkumar Prabhakaran, Emily Den- ton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020. Social biases in NLP models as barriers for persons with disabilities. In Proceedings of the 58th",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Annual Meeting of the Association for Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "5491--5501",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computational Linguistics, pages 5491-5501, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Measuring bias in contextualized word representations",
"authors": [
{
"first": "Keita",
"middle": [],
"last": "Kurita",
"suffix": ""
},
{
"first": "Nidhi",
"middle": [],
"last": "Vyas",
"suffix": ""
},
{
"first": "Ayush",
"middle": [],
"last": "Pareek",
"suffix": ""
},
{
"first": "Alan",
"middle": [
"W"
],
"last": "Black",
"suffix": ""
},
{
"first": "Yulia",
"middle": [],
"last": "Tsvetkov",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "166--172",
"other_ids": {
"DOI": [
"10.18653/v1/W19-3823"
]
},
"num": null,
"urls": [],
"raw_text": "Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019. Measuring bias in con- textualized word representations. In Proceedings of the First Workshop on Gender Bias in Natural Lan- guage Processing, pages 166-172, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Are we consistently biased? multidimensional analysis of biases in distributional word vectors",
"authors": [
{
"first": "Anne",
"middle": [],
"last": "Lauscher",
"suffix": ""
},
{
"first": "Goran",
"middle": [],
"last": "Glava\u0161",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)",
"volume": "",
"issue": "",
"pages": "85--91",
"other_ids": {
"DOI": [
"10.18653/v1/S19-1010"
]
},
"num": null,
"urls": [],
"raw_text": "Anne Lauscher and Goran Glava\u0161. 2019. Are we con- sistently biased? multidimensional analysis of biases in distributional word vectors. In Proceedings of the Eighth Joint Conference on Lexical and Computa- tional Semantics (*SEM 2019), pages 85-91, Min- neapolis, Minnesota. Association for Computational Linguistics.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Sustainable modular debiasing of language models",
"authors": [
{
"first": "Anne",
"middle": [],
"last": "Lauscher",
"suffix": ""
},
{
"first": "Tobias",
"middle": [],
"last": "Lueken",
"suffix": ""
},
{
"first": "Goran",
"middle": [],
"last": "Glava\u0161",
"suffix": ""
}
],
"year": 2021,
"venue": "Findings of the Association for Computational Linguistics: EMNLP 2021",
"volume": "",
"issue": "",
"pages": "4782--4797",
"other_ids": {
"DOI": [
"10.18653/v1/2021.findings-emnlp.411"
]
},
"num": null,
"urls": [],
"raw_text": "Anne Lauscher, Tobias Lueken, and Goran Glava\u0161. 2021. Sustainable modular debiasing of language models. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4782-4797, Punta Cana, Dominican Republic. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Towards debiasing sentence representations",
"authors": [
{
"first": "Irene",
"middle": [
"Mengze"
],
"last": "Paul Pu Liang",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Chong",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Ruslan",
"middle": [],
"last": "Lim",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Salakhutdinov",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "5502--5515",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.488"
]
},
"num": null,
"urls": [],
"raw_text": "Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis- Philippe Morency. 2020. Towards debiasing sentence representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 5502-5515, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings",
"authors": [
{
"first": "Thomas",
"middle": [],
"last": "Manzini",
"suffix": ""
},
{
"first": "Yao",
"middle": [],
"last": "Lim",
"suffix": ""
},
{
"first": "Alan",
"middle": [
"W"
],
"last": "Chong",
"suffix": ""
},
{
"first": "Yulia",
"middle": [],
"last": "Black",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Tsvetkov",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "615--621",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1062"
]
},
"num": null,
"urls": [],
"raw_text": "Thomas Manzini, Lim Yao Chong, Alan W Black, and Yulia Tsvetkov. 2019. Black is to criminal as cau- casian is to police: Detecting and removing multi- class bias in word embeddings. In Proceedings of the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 615-621, Minneapolis, Min- nesota. Association for Computational Linguistics.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "On measuring social biases in sentence encoders",
"authors": [
{
"first": "Chandler",
"middle": [],
"last": "May",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Shikha",
"middle": [],
"last": "Bordia",
"suffix": ""
},
{
"first": "Samuel",
"middle": [
"R"
],
"last": "Bowman",
"suffix": ""
},
{
"first": "Rachel",
"middle": [],
"last": "Rudinger",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "622--628",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1063"
]
},
"num": null,
"urls": [],
"raw_text": "Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019. On measuring social biases in sentence encoders. In Proceedings of the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 622-628, Minneapolis, Min- nesota. Association for Computational Linguistics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "StereoSet: Measuring stereotypical bias in pretrained language models",
"authors": [
{
"first": "Moin",
"middle": [],
"last": "Nadeem",
"suffix": ""
},
{
"first": "Anna",
"middle": [],
"last": "Bethke",
"suffix": ""
},
{
"first": "Siva",
"middle": [],
"last": "Reddy",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "5356--5371",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.416"
]
},
"num": null,
"urls": [],
"raw_text": "Moin Nadeem, Anna Bethke, and Siva Reddy. 2021. StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5356-5371, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "CrowS-pairs: A challenge dataset for measuring social biases in masked language models",
"authors": [
{
"first": "Nikita",
"middle": [],
"last": "Nangia",
"suffix": ""
},
{
"first": "Clara",
"middle": [],
"last": "Vania",
"suffix": ""
},
{
"first": "Rasika",
"middle": [],
"last": "Bhalerao",
"suffix": ""
},
{
"first": "Samuel",
"middle": [
"R"
],
"last": "Bowman",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "1953--1967",
"other_ids": {
"DOI": [
"10.18653/v1/2020.emnlp-main.154"
]
},
"num": null,
"urls": [],
"raw_text": "Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020. CrowS-pairs: A chal- lenge dataset for measuring social biases in masked language models. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 1953-1967, Online. As- sociation for Computational Linguistics.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "HONEST: Measuring hurtful sentence completion in language models",
"authors": [
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": ""
},
{
"first": "Federico",
"middle": [],
"last": "Bianchi",
"suffix": ""
},
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "2398--2406",
"other_ids": {
"DOI": [
"10.18653/v1/2021.naacl-main.191"
]
},
"num": null,
"urls": [],
"raw_text": "Debora Nozza, Federico Bianchi, and Dirk Hovy. 2021. HONEST: Measuring hurtful sentence completion in language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2398-2406, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals",
"authors": [
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": ""
},
{
"first": "Federico",
"middle": [],
"last": "Bianchi",
"suffix": ""
},
{
"first": "Anne",
"middle": [],
"last": "Lauscher",
"suffix": ""
},
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2022,
"venue": "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Debora Nozza, Federico Bianchi, Anne Lauscher, and Dirk Hovy. 2022. Measuring Harmful Sentence Com- pletion in Language Models for LGBTQIA+ Individ- uals. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and In- clusion. Association for Computational Linguistics.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Unintended bias in misogyny detection. WI '19",
"authors": [
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": ""
},
{
"first": "Claudia",
"middle": [],
"last": "Volpetti",
"suffix": ""
},
{
"first": "Elisabetta",
"middle": [],
"last": "Fersini",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "149--155",
"other_ids": {
"DOI": [
"10.1145/3350546.3352512"
]
},
"num": null,
"urls": [],
"raw_text": "Debora Nozza, Claudia Volpetti, and Elisabetta Fersini. 2019. Unintended bias in misogyny detection. WI '19, page 149-155, New York, NY, USA. Association for Computing Machinery.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Probing toxic content in large pre-trained language models",
"authors": [
{
"first": "Nedjma",
"middle": [],
"last": "Ousidhoum",
"suffix": ""
},
{
"first": "Xinran",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Tianqing",
"middle": [],
"last": "Fang",
"suffix": ""
},
{
"first": "Yangqiu",
"middle": [],
"last": "Song",
"suffix": ""
},
{
"first": "Dit-Yan",
"middle": [],
"last": "Yeung",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "4262--4274",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.329"
]
},
"num": null,
"urls": [],
"raw_text": "Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, and Dit-Yan Yeung. 2021. Probing toxic content in large pre-trained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Lan- guage Processing (Volume 1: Long Papers), pages 4262-4274, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Beyond accuracy: Behavioral testing of NLP models with CheckList",
"authors": [
{
"first": "Tongshuang",
"middle": [],
"last": "Marco Tulio Ribeiro",
"suffix": ""
},
{
"first": "Carlos",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Sameer",
"middle": [],
"last": "Guestrin",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Singh",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "4902--4912",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.442"
]
},
"num": null,
"urls": [],
"raw_text": "Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Be- havioral testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 4902- 4912, Online. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "HateCheck: Functional tests for hate speech detection models",
"authors": [
{
"first": "Paul",
"middle": [],
"last": "R\u00f6ttger",
"suffix": ""
},
{
"first": "Bertie",
"middle": [],
"last": "Vidgen",
"suffix": ""
},
{
"first": "Dong",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "Zeerak",
"middle": [],
"last": "Waseem",
"suffix": ""
},
{
"first": "Helen",
"middle": [],
"last": "Margetts",
"suffix": ""
},
{
"first": "Janet",
"middle": [],
"last": "Pierrehumbert",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "41--58",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.4"
]
},
"num": null,
"urls": [],
"raw_text": "Paul R\u00f6ttger, Bertie Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, and Janet Pierrehumbert. 2021. HateCheck: Functional tests for hate speech detection models. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 41-58, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Predictive biases in natural language processing models: A conceptual framework and overview",
"authors": [
{
"first": "",
"middle": [],
"last": "Deven Santosh",
"suffix": ""
},
{
"first": "H",
"middle": [
"Andrew"
],
"last": "Shah",
"suffix": ""
},
{
"first": "Dirk",
"middle": [],
"last": "Schwartz",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "5248--5264",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.468"
]
},
"num": null,
"urls": [],
"raw_text": "Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020. Predictive biases in natural language processing models: A conceptual framework and overview. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pages 5248-5264, Online. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Societal biases in language generation: Progress and challenges",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Sheng",
"suffix": ""
},
{
"first": "Kai-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Prem",
"middle": [],
"last": "Natarajan",
"suffix": ""
},
{
"first": "Nanyun",
"middle": [],
"last": "Peng",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "4275--4293",
"other_ids": {
"DOI": [
"10.18653/v1/2021.acl-long.330"
]
},
"num": null,
"urls": [],
"raw_text": "Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2021. Societal biases in language generation: Progress and challenges. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4275-4293, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "The woman worked as a babysitter: On biases in language generation",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Sheng",
"suffix": ""
},
{
"first": "Kai-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Premkumar",
"middle": [],
"last": "Natarajan",
"suffix": ""
},
{
"first": "Nanyun",
"middle": [],
"last": "Peng",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "3407--3412",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1339"
]
},
"num": null,
"urls": [],
"raw_text": "Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019. The woman worked as a babysitter: On biases in language generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 3407- 3412, Hong Kong, China. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "A survey on gender bias in natural language processing",
"authors": [
{
"first": "Karolina",
"middle": [],
"last": "Stanczak",
"suffix": ""
},
{
"first": "Isabelle",
"middle": [],
"last": "Augenstein",
"suffix": ""
}
],
"year": 2021,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2112.14168"
]
},
"num": null,
"urls": [],
"raw_text": "Karolina Stanczak and Isabelle Augenstein. 2021. A survey on gender bias in natural language processing. arXiv preprint arXiv:2112.14168.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "What are the biases in my word embedding?",
"authors": [
{
"first": "Nathaniel",
"middle": [],
"last": "Swinger",
"suffix": ""
},
{
"first": "Maria",
"middle": [],
"last": "De-Arteaga",
"suffix": ""
},
{
"first": "Neil",
"middle": [
"Thomas"
],
"last": "Heffernan",
"suffix": ""
},
{
"first": "I",
"middle": [
"V"
],
"last": "Mark",
"suffix": ""
},
{
"first": "D",
"middle": [
"M"
],
"last": "Leiserson",
"suffix": ""
},
{
"first": "Adam Tauman",
"middle": [],
"last": "Kalai",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES '19",
"volume": "",
"issue": "",
"pages": "305--311",
"other_ids": {
"DOI": [
"10.1145/3306618.3314270"
]
},
"num": null,
"urls": [],
"raw_text": "Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark DM Leiserson, and Adam Tau- man Kalai. 2019. What are the biases in my word embedding? In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES '19, page 305-311, New York, NY, USA. Association for Computing Machinery.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Low frequency names exhibit bias and overfitting in contextualizing language models",
"authors": [
{
"first": "Robert",
"middle": [],
"last": "Wolfe",
"suffix": ""
},
{
"first": "Aylin",
"middle": [],
"last": "Caliskan",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "518--532",
"other_ids": {
"DOI": [
"10.18653/v1/2021.emnlp-main.41"
]
},
"num": null,
"urls": [],
"raw_text": "Robert Wolfe and Aylin Caliskan. 2021. Low frequency names exhibit bias and overfitting in contextualizing language models. In Proceedings of the 2021 Con- ference on Empirical Methods in Natural Language Processing, pages 518-532, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Hurtful words: Quantifying biases in clinical contextual word embeddings",
"authors": [
{
"first": "Haoran",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Amy",
"middle": [
"X"
],
"last": "Lu",
"suffix": ""
},
{
"first": "Mohamed",
"middle": [],
"last": "Abdalla",
"suffix": ""
},
{
"first": "Matthew",
"middle": [],
"last": "Mcdermott",
"suffix": ""
},
{
"first": "Marzyeh",
"middle": [],
"last": "Ghassemi",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the ACM Conference on Health, Inference, and Learning, CHIL '20",
"volume": "",
"issue": "",
"pages": "110--120",
"other_ids": {
"DOI": [
"10.1145/3368555.3384448"
]
},
"num": null,
"urls": [],
"raw_text": "Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew McDermott, and Marzyeh Ghassemi. 2020. Hurtful words: Quantifying biases in clinical con- textual word embeddings. In Proceedings of the ACM Conference on Health, Inference, and Learn- ing, CHIL '20, page 110-120, New York, NY, USA. Association for Computing Machinery.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"text": "The figure shows an example of the possible integration of Social Bias tests into a development pipeline.",
"num": null,
"type_str": "figure"
}
}
}
} |