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{\textstyle x^{\prime }} for a given image x {\textstyle x} that satisfies the attack objectives. Initialize x {\textstyle x} to some point where S ( x ) > 0 {\textstyle S(x)>0} Iterate below Boundary search Gradient update Compute the gradient Find the step size Boundary search uses a modified binary search to find th... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack | 270 | 1,135 | null |
However, since HopSkipJump is a proposed black box attack and the iterative algorithm above requires the calculation of a gradient in the second iterative step (which black box attacks do not have access to), the authors propose a solution to gradient calculation that requires only the model's output predictions alone.... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack | 305 | 1,050 | null |
Section: Specific attack types > Adversarial examples > White box attacks > Fast gradient sign method. One of the first proposed attacks for generating adversarial examples was proposed by Google researchers Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. The attack was called fast gradient sign method (FGSM... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > White box attacks > Fast gradient sign method | 254 | 1,043 | null |
Shown below is the equation to generate an adversarial example where x {\textstyle x} is the original image, ϵ {\textstyle \epsilon } is a very small number, Δ x {\textstyle \Delta _{x}} is the gradient function, J {\textstyle J} is the loss function, θ {\textstyle \theta } is the model weights, and y {\textstyle y} is... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > White box attacks > Fast gradient sign method | 308 | 1,206 | null |
Section: Specific attack types > Adversarial examples > White box attacks > Carlini & Wagner (C&W). In an effort to analyze existing adversarial attacks and defenses, researchers at the University of California, Berkeley, Nicholas Carlini and David Wagner in 2016 propose a faster and more robust method to generate adve... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > White box attacks > Carlini & Wagner (C&W) | 256 | 912 | null |
The attack proposed by Carlini and Wagner begins with trying to solve a difficult non-linear optimization equation: min ( | | δ | | p ) subject to C ( x + δ ) = t , x + δ ∈ [ 0 , 1 ] n {\displaystyle \min(||\delta ||_{p}){\text{ subject to }}C(x+\delta )=t,x+\delta \in [0,1]^{n}} Here the objective is to minimize the n... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > White box attacks > Carlini & Wagner (C&W) | 486 | 1,387 | null |
However instead of directly the above equation, Carlini and Wagner propose using a new function f {\textstyle f} such that: C ( x + δ ) = t ⟺ f ( x + δ ) ≤ 0 {\displaystyle C(x+\delta )=t\iff f(x+\delta )\leq 0} This condenses the first equation to the problem below: min ( | | δ | | p ) subject to f ( x + δ ) ≤ 0 , x +... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > White box attacks > Carlini & Wagner (C&W) | 454 | 1,356 | null |
Section: Defenses > Mechanisms. A number of defense mechanisms against evasion, poisoning, and privacy attacks have been proposed, including: Secure learning algorithms Byzantine-resilient algorithms Multiple classifier systems AI-written algorithms. AIs that explore the training environment; for example, in image reco... | Wikipedia - Adversarial machine learning - Defenses > Mechanisms | 188 | 1,007 | null |
Section: Key features. Modern AI/ML platforms typically include: End-to-end workflow support: Data preparation: Tools for cleaning, labeling, and augmenting datasets. Model building: Libraries for designing neural networks (e.g., PyTorch, TensorFlow integrations). Training & Optimization: Distributed training, hyperpar... | Wikipedia - AI/ML Development Platform - Key features | 231 | 954 | null |
Article: AIXI. AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI ... | Wikipedia - AIXI - Summary | 193 | 1,020 | null |
Section: Definition. AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ {\displaystyle \mu } . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifes... | Wikipedia - AIXI - Definition | 291 | 883 | null |
. . a t − 1 o t − 1 r t − 1 a t ) {\displaystyle \mu (o_{t}r_{t}|a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t})} , where a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t {\displaystyle a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t}} is the "history" of actions, observations and rewards. The environment μ {\displaystyle \mu }... | Wikipedia - AIXI - Definition | 300 | 887 | null |
Furthermore, note again that μ {\displaystyle \mu } is computable, that is, the observations and rewards received by the agent from the environment μ {\displaystyle \mu } can be computed by some program (which runs on a Turing machine), given the past actions of the AIXI agent. The only goal of the AIXI agent is to max... | Wikipedia - AIXI - Definition | 262 | 875 | null |
The AIXI agent is associated with a stochastic policy π : ( A × E ) ∗ → A {\displaystyle \pi :({\mathcal {A}}\times {\mathcal {E}})^{*}\rightarrow {\mathcal {A}}} , which is the function it uses to choose actions at every time step, where A {\displaystyle {\mathcal {A}}} is the space of all possible actions that AIXI c... | Wikipedia - AIXI - Definition | 259 | 779 | null |
The environment (or probability distribution) μ {\displaystyle \mu } can also be thought of as a stochastic policy (which is a function): μ : ( A × E ) ∗ × A → E {\displaystyle \mu :({\mathcal {A}}\times {\mathcal {E}})^{*}\times {\mathcal {A}}\rightarrow {\mathcal {E}}} , where the ∗ {\displaystyle *} is the Kleene st... | Wikipedia - AIXI - Definition | 212 | 634 | null |
o t − 1 r t − 1 {\displaystyle o_{1}r_{1}...o_{t-1}r_{t-1}} (which can be abbreviated as e < t {\displaystyle e_{<t}} ), chooses and executes in the environment the action, a t {\displaystyle a_{t}} , defined as follows: a t := arg max a t ∑ o t r t … max a m ∑ o m r m [ r t + … + r m ] ∑ q : U ( q , a 1 … a m ) = o ... | Wikipedia - AIXI - Definition | 350 | 687 | null |
( ∑ q : U ( q , a 1 … a m ) = o 1 r 1 … o m r m 2 − length ( q ) ) ) ) {\displaystyle a_{t}:=\arg \max _{a_{t}}\left(\sum _{o_{t}r_{t}}\ldots \left(\max _{a_{m}}\sum _{o_{m}r_{m}}[r_{t}+\ldots +r_{m}]\left(\sum _{q:\;U(q,a_{1}\ldots a_{m})=o_{1}r_{1}\ldots o_{m}r_{m}}2^{-{\textrm {length}}(q)}\right)\right)\right)} Int... | Wikipedia - AIXI - Definition | 350 | 810 | null |
e < t {\displaystyle e_{<t}} ) that can generate that future, and then picks the action that maximizes expected future rewards. Let us break this definition down in order to attempt to fully understand it. o t r t {\displaystyle o_{t}r_{t}} is the "percept" (which consists of the observation o t {\displaystyle o_{t}} a... | Wikipedia - AIXI - Definition | 265 | 863 | null |
r t + … + r m {\displaystyle r_{t}+\ldots +r_{m}} is the sum of rewards from time step t {\displaystyle t} to time step m {\displaystyle m} , so AIXI needs to look into the future to choose its action at time step t {\displaystyle t} . U {\displaystyle U} denotes a monotone universal Turing machine, and q {\displaystyl... | Wikipedia - AIXI - Definition | 323 | 1,134 | null |
Note that, in general, the program which "models" the current and actual environment (where AIXI needs to act) is unknown because the current environment is also unknown. length ( q ) {\displaystyle {\textrm {length}}(q)} is the length of the program q {\displaystyle q} (which is encoded as a string of bits). Note that... | Wikipedia - AIXI - Definition | 315 | 870 | null |
Hence, in the definition above, ∑ q : U ( q , a 1 … a m ) = o 1 r 1 … o m r m 2 − length ( q ) {\displaystyle \sum _{q:\;U(q,a_{1}\ldots a_{m})=o_{1}r_{1}\ldots o_{m}r_{m}}2^{-{\textrm {length}}(q)}} should be interpreted as a mixture (in this case, a sum) over all computable environments (which are consistent with the... | Wikipedia - AIXI - Definition | 304 | 736 | null |
Note that a 1 … a m {\displaystyle a_{1}\ldots a_{m}} can also be written as a 1 … a t − 1 a t … a m {\displaystyle a_{1}\ldots a_{t-1}a_{t}\ldots a_{m}} , and a 1 … a t − 1 = a < t {\displaystyle a_{1}\ldots a_{t-1}=a_{<t}} is the sequence of actions already executed in the environment by the AIXI agent. Similarly, o ... | Wikipedia - AIXI - Definition | 322 | 725 | null |
Let us now put all these components together in order to understand this equation or definition. At time step t, AIXI chooses the action a t {\displaystyle a_{t}} where the function ∑ o t r t … max a m ∑ o m r m [ r t + … + r m ] ∑ q : U ( q , a 1 … a m ) = o 1 r 1 … o m r m 2 − length ( q ) {\displaystyle \sum _{o_{t}... | Wikipedia - AIXI - Definition | 231 | 495 | null |
Section: Optimality. AIXI's performance is measured by the expected total number of rewards it receives. AIXI has been proven to be optimal in the following ways. Pareto optimality: there is no other agent that performs at least as well as AIXI in all environments while performing strictly better in at least one enviro... | Wikipedia - AIXI - Optimality | 304 | 1,437 | null |
Section: Definition. Given a portfolio P {\displaystyle {\mathcal {P}}} of algorithms A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} , a set of instances i ∈ I {\displaystyle i\in {\mathcal {I}}} and a cost metric m : P × I → R {\displaystyle m:{\mathcal {P}}\times {\mathcal {I}}\to \mathbb {R} } , the algorith... | Wikipedia - Algorithm selection - Definition | 244 | 658 | null |
Section: Examples > Boolean satisfiability problem (and other hard combinatorial problems). A well-known application of algorithm selection is the Boolean satisfiability problem. Here, the portfolio of algorithms is a set of (complementary) SAT solvers, the instances are Boolean formulas, the cost metric is for example... | Wikipedia - Algorithm selection - Examples > Boolean satisfiability problem (and other hard combinatorial problems) | 173 | 732 | null |
Section: Instance features > Static vs. probing features. We distinguish between two kinds of features: Static features are in most cases some counts and statistics (e.g., clauses-to-variables ratio in SAT). These features ranges from very cheap features (e.g. number of variables) to very complex features (e.g., statis... | Wikipedia - Algorithm selection - Instance features > Static vs. probing features | 151 | 705 | null |
Section: Instance features > Feature costs. Depending on the used performance metric m {\displaystyle m} , feature computation can be associated with costs. For example, if we use running time as performance metric, we include the time to compute our instance features into the performance of an algorithm selection syst... | Wikipedia - Algorithm selection - Instance features > Feature costs | 218 | 1,119 | null |
Section: Approaches > Pairwise cost-sensitive classification approach. A common approach for multi-class classification is to learn pairwise models between every pair of classes (here algorithms) and choose the class that was predicted most often by the pairwise models. We can weight the instances of the pairwise predi... | Wikipedia - Algorithm selection - Approaches > Pairwise cost-sensitive classification approach | 225 | 886 | null |
Section: Requirements. The algorithm selection problem can be effectively applied under the following assumptions: The portfolio P {\displaystyle {\mathcal {P}}} of algorithms is complementary with respect to the instance set I {\displaystyle {\mathcal {I}}} , i.e., there is no single algorithm A ∈ P {\displaystyle {\m... | Wikipedia - Algorithm selection - Requirements | 190 | 832 | null |
Article: Algorithmic bias. Algorithmic bias describes systematic and repeatable harmful tendency in a computerized sociotechnical system to create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, includin... | Wikipedia - Algorithmic bias - Summary | 308 | 1,608 | null |
Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Bias can enter into a... | Wikipedia - Algorithmic bias - Summary | 347 | 1,873 | null |
Section: Definitions. Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate output.: 13 For a rigorous technical introduction, see Algorithms. Advances in computer hardware have led to an increased ab... | Wikipedia - Algorithmic bias - Definitions | 301 | 1,648 | null |
Section: Methods. Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria.: 3 Next, programmers assign priorities, or hierarchies, for how a program assesses and sor... | Wikipedia - Algorithmic bias - Methods | 339 | 1,764 | null |
Section: History > Early critiques. The earliest computer programs were designed to mimic human reasoning and deductions, and were deemed to be functioning when they successfully and consistently reproduced that human logic. In his 1976 book Computer Power and Human Reason, artificial intelligence pioneer Joseph Weizen... | Wikipedia - Algorithmic bias - History > Early critiques | 349 | 1,799 | null |
Crucially, the tourist has no basis of understanding how or why he arrived at his destination, and a successful arrival does not mean the process is accurate or reliable.: 226 An early example of algorithmic bias resulted in as many as 60 women and ethnic minorities denied entry to St. George's Hospital Medical School ... | Wikipedia - Algorithmic bias - History > Early critiques | 342 | 1,737 | null |
Section: History > Contemporary critiques and responses. Though well-designed algorithms frequently determine outcomes that are equally (or more) equitable than the decisions of human beings, cases of bias still occur, and are difficult to predict and analyze. The complexity of analyzing algorithmic bias has grown alon... | Wikipedia - Algorithmic bias - History > Contemporary critiques and responses | 304 | 1,558 | null |
This neutrality can also be misrepresented by the language used by experts and the media when results are presented to the public. For example, a list of news items selected and presented as "trending" or "popular" may be created based on significantly wider criteria than just their popularity.: 14 Because of their con... | Wikipedia - Algorithmic bias - History > Contemporary critiques and responses | 252 | 1,350 | null |
Consequently, incorporating fair algorithmic tools into decision-making processes does not automatically eliminate human biases. Concerns over the impact of algorithms on society have led to the creation of working groups in organizations such as Google and Microsoft, which have co-created a working group named Fairnes... | Wikipedia - Algorithmic bias - History > Contemporary critiques and responses | 157 | 914 | null |
Section: Types > Pre-existing. Pre-existing bias in an algorithm is a consequence of underlying social and institutional ideologies. Such ideas may influence or create personal biases within individual designers or programmers. Such prejudices can be explicit and conscious, or implicit and unconscious.: 334 : 294 Poorl... | Wikipedia - Algorithmic bias - Types > Pre-existing | 311 | 1,576 | null |
Section: Types > Machine learning bias > Language bias. Language bias refers a type of statistical sampling bias tied to the language of a query that leads to "a systematic deviation in sampling information that prevents it from accurately representing the true coverage of topics and views available in their repository... | Wikipedia - Algorithmic bias - Types > Machine learning bias > Language bias | 216 | 1,152 | null |
Section: Types > Machine learning bias > Stereotyping. Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that homogenize, or unfairly generalize or caricature groups of people, sometimes in harmful... | Wikipedia - Algorithmic bias - Types > Machine learning bias > Stereotyping | 177 | 950 | null |
Section: Types > Machine learning bias > Racial bias. Racial bias refers to the tendency of machine learning models to produce outcomes that unfairly discriminate against or stereotype individuals based on race or ethnicity. This bias often stems from training data that reflects historical and systemic inequalities. Fo... | Wikipedia - Algorithmic bias - Types > Machine learning bias > Racial bias | 160 | 906 | null |
Section: Types > Technical. Technical bias emerges through limitations of a program, computational power, its design, or other constraint on the system.: 332 Such bias can also be a restraint of design, for example, a search engine that shows three results per screen can be understood to privilege the top three results... | Wikipedia - Algorithmic bias - Types > Technical | 306 | 1,602 | null |
This could create an incomplete understanding of a crime scene, for example, potentially mistaking bystanders for those who commit the crime.: 574 Lastly, technical bias can be created by attempting to formalize decisions into concrete steps on the assumption that human behavior works in the same way. For example, soft... | Wikipedia - Algorithmic bias - Types > Technical | 247 | 1,256 | null |
Section: Types > Emergent. Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts.: 334 Algorithms may not have been adjusted to consider new forms of knowledge, such as new drugs or medical breakthroughs, new laws, business models, or shifting cultural norms.: 334, 336 T... | Wikipedia - Algorithmic bias - Types > Emergent | 309 | 1,608 | null |
Section: Types > Emergent > Correlations. Unpredictable correlations can emerge when large data sets are compared to each other. For example, data collected about web-browsing patterns may align with signals marking sensitive data (such as race or sexual orientation). By selecting according to certain behavior or brows... | Wikipedia - Algorithmic bias - Types > Emergent > Correlations | 173 | 938 | null |
Section: Types > Emergent > Unanticipated uses. Emergent bias can occur when an algorithm is used by unanticipated audiences. For example, machines may require that users can read, write, or understand numbers, or relate to an interface using metaphors that they do not understand.: 334 These exclusions can become compo... | Wikipedia - Algorithmic bias - Types > Emergent > Unanticipated uses | 296 | 1,510 | null |
Section: Types > Emergent > Feedback loops. Emergent bias may also create a feedback loop, or recursion, if data collected for an algorithm results in real-world responses which are fed back into the algorithm. For example, simulations of the predictive policing software (PredPol), deployed in Oakland, California, sugg... | Wikipedia - Algorithmic bias - Types > Emergent > Feedback loops | 312 | 1,732 | null |
Section: Impact > Commercial influences. Corporate algorithms could be skewed to invisibly favor financial arrangements or agreements between companies, without the knowledge of a user who may mistake the algorithm as being impartial. For example, American Airlines created a flight-finding algorithm in the 1980s. The s... | Wikipedia - Algorithmic bias - Impact > Commercial influences | 190 | 1,036 | null |
Section: Impact > Voting behavior. A series of studies about undecided voters in the US and in India found that search engine results were able to shift voting outcomes by about 20%. The researchers concluded that candidates have "no means of competing" if an algorithm, with or without intent, boosted page listings for... | Wikipedia - Algorithmic bias - Impact > Voting behavior | 173 | 858 | null |
Section: Impact > Gender discrimination. In 2016, the professional networking site LinkedIn was discovered to recommend male variations of women's names in response to search queries. The site did not make similar recommendations in searches for male names. For example, "Andrea" would bring up a prompt asking if users ... | Wikipedia - Algorithmic bias - Impact > Gender discrimination | 339 | 1,766 | null |
Section: Impact > Racial and ethnic discrimination. Algorithms have been criticized as a method for obscuring racial prejudices in decision-making.: 158 Because of how certain races and ethnic groups were treated in the past, data can often contain hidden biases. For example, black people are likely to receive longer s... | Wikipedia - Algorithmic bias - Impact > Racial and ethnic discrimination | 336 | 1,777 | null |
In 2019, a research study revealed that a healthcare algorithm sold by Optum favored white patients over sicker black patients. The algorithm predicts how much patients would cost the health-care system in the future. However, cost is not race-neutral, as black patients incurred about $1,800 less in medical costs per y... | Wikipedia - Algorithmic bias - Impact > Racial and ethnic discrimination | 320 | 1,705 | null |
Section: Impact > Racial and ethnic discrimination > Law enforcement and legal proceedings. Algorithms already have numerous applications in legal systems. An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that ... | Wikipedia - Algorithmic bias - Impact > Racial and ethnic discrimination > Law enforcement and legal proceedings | 345 | 1,680 | null |
Section: Impact > Racial and ethnic discrimination > Online hate speech. In 2017 a Facebook algorithm designed to remove online hate speech was found to advantage white men over black children when assessing objectionable content, according to internal Facebook documents. The algorithm, which is a combination of comput... | Wikipedia - Algorithmic bias - Impact > Racial and ethnic discrimination > Online hate speech | 343 | 1,719 | null |
Section: Impact > Racial and ethnic discrimination > Surveillance. Surveillance camera software may be considered inherently political because it requires algorithms to distinguish normal from abnormal behaviors, and to determine who belongs in certain locations at certain times.: 572 The ability of such algorithms to ... | Wikipedia - Algorithmic bias - Impact > Racial and ethnic discrimination > Surveillance | 294 | 1,616 | null |
Section: Impact > Discrimination against the LGBTQ community. In 2011, users of the gay hookup application Grindr reported that the Android store's recommendation algorithm was linking Grindr to applications designed to find sex offenders, which critics said inaccurately related homosexuality with pedophilia. Writer Mi... | Wikipedia - Algorithmic bias - Impact > Discrimination against the LGBTQ community | 333 | 1,794 | null |
Although the solution to this issue would appear to be including trans individuals in training sets for machine learning models, an instance of trans YouTube videos that were collected to be used in training data did not receive consent from the trans individuals that were included in the videos, which created an issue... | Wikipedia - Algorithmic bias - Impact > Discrimination against the LGBTQ community | 190 | 1,007 | null |
Section: Impact > Disability discrimination. While the modalities of algorithmic fairness have been judged on the basis of different aspects of bias – like gender, race and socioeconomic status, disability often is left out of the list. The marginalization people with disabilities currently face in society is being tra... | Wikipedia - Algorithmic bias - Impact > Disability discrimination | 313 | 1,768 | null |
Therefore, machine learning models are trained inequitably and artificial intelligent systems perpetuate more algorithmic bias. For example, if people with speech impairments are not included in training voice control features and smart AI assistants –they are unable to use the feature or the responses received from a ... | Wikipedia - Algorithmic bias - Impact > Disability discrimination | 189 | 1,087 | null |
Section: Obstacles to research > Defining fairness. Literature on algorithmic bias has focused on the remedy of fairness, but definitions of fairness are often incompatible with each other and the realities of machine learning optimization. For example, defining fairness as an "equality of outcomes" may simply refer to... | Wikipedia - Algorithmic bias - Obstacles to research > Defining fairness | 160 | 916 | null |
Section: Obstacles to research > Complexity. Algorithmic processes are complex, often exceeding the understanding of the people who use them.: 2 : 7 Large-scale operations may not be understood even by those involved in creating them. The methods and processes of contemporary programs are often obscured by the inabilit... | Wikipedia - Algorithmic bias - Obstacles to research > Complexity | 327 | 1,705 | null |
Furthermore, large teams of programmers may operate in relative isolation from one another, and be unaware of the cumulative effects of small decisions within connected, elaborate algorithms.: 118 Not all code is original, and may be borrowed from other libraries, creating a complicated set of relationships between dat... | Wikipedia - Algorithmic bias - Obstacles to research > Complexity | 215 | 1,199 | null |
Section: Obstacles to research > Lack of transparency. Commercial algorithms are proprietary, and may be treated as trade secrets.: 2 : 7 : 183 Treating algorithms as trade secrets protects companies, such as search engines, where a transparent algorithm might reveal tactics to manipulate search rankings.: 366 This mak... | Wikipedia - Algorithmic bias - Obstacles to research > Lack of transparency | 150 | 815 | null |
Section: Obstacles to research > Lack of data about sensitive categories. A significant barrier to understanding the tackling of bias in practice is that categories, such as demographics of individuals protected by anti-discrimination law, are often not explicitly considered when collecting and processing data. In some... | Wikipedia - Algorithmic bias - Obstacles to research > Lack of data about sensitive categories | 318 | 1,744 | null |
Section: Solutions > Technical. There have been several attempts to create methods and tools that can detect and observe biases within an algorithm. These emergent fields focus on tools which are typically applied to the (training) data used by the program rather than the algorithm's internal processes. These methods m... | Wikipedia - Algorithmic bias - Solutions > Technical | 342 | 1,834 | null |
Currently, a new IEEE standard is being drafted that aims to specify methodologies which help creators of algorithms eliminate issues of bias and articulate transparency (i.e. to authorities or end users) about the function and possible effects of their algorithms. The project was approved February 2017 and is sponsore... | Wikipedia - Algorithmic bias - Solutions > Technical | 190 | 1,066 | null |
Section: Solutions > Transparency and monitoring. Ethics guidelines on AI point to the need for accountability, recommending that steps be taken to improve the interpretability of results. Such solutions include the consideration of the "right to understanding" in machine learning algorithms, and to resist deployment o... | Wikipedia - Algorithmic bias - Solutions > Transparency and monitoring | 244 | 1,384 | null |
Section: Solutions > Diversity and inclusion. Amid concerns that the design of AI systems is primarily the domain of white, male engineers, a number of scholars have suggested that algorithmic bias may be minimized by expanding inclusion in the ranks of those designing AI systems. For example, just 12% of machine learn... | Wikipedia - Algorithmic bias - Solutions > Diversity and inclusion | 183 | 990 | null |
Section: Solutions > Interdisciplinarity and Collaboration. Integrating interdisciplinarity and collaboration in developing of AI systems can play a critical role in tackling algorithmic bias. Integrating insights, expertise, and perspectives from disciplines outside of computer science can foster a better understandin... | Wikipedia - Algorithmic bias - Solutions > Interdisciplinarity and Collaboration | 284 | 1,673 | null |
Section: Regulation > Europe. The General Data Protection Regulation (GDPR), the European Union's revised data protection regime that was implemented in 2018, addresses "Automated individual decision-making, including profiling" in Article 22. These rules prohibit "solely" automated decisions which have a "significant"... | Wikipedia - Algorithmic bias - Regulation > Europe | 332 | 1,738 | null |
Section: Regulation > United States. The United States has no general legislation controlling algorithmic bias, approaching the problem through various state and federal laws that might vary by industry, sector, and by how an algorithm is used. Many policies are self-enforced or controlled by the Federal Trade Commissi... | Wikipedia - Algorithmic bias - Regulation > United States | 334 | 1,866 | null |
leadership in artificial intelligence. The initiative highlights the importance of sustained AI research and development, ethical standards, workforce training, and the protection of critical AI technologies. This aligns with broader efforts to ensure transparency, accountability, and innovation in AI systems across pu... | Wikipedia - Algorithmic bias - Regulation > United States | 180 | 1,015 | null |
Section: The Fisher parametric inference problem. Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1... | Wikipedia - Algorithmic inference - The Fisher parametric inference problem | 215 | 1,040 | null |
Section: The classic solution. Fisher fought hard to defend the difference and superiority of his notion of parameter distribution in comparison to analogous notions, such as Bayes' posterior distribution, Fraser's constructive probability and Neyman's confidence intervals. For half a century, Neyman's confidence inter... | Wikipedia - Algorithmic inference - The classic solution | 152 | 825 | null |
Working with statistics S μ = ∑ i = 1 m X i {\displaystyle S_{\mu }=\sum _{i=1}^{m}X_{i}} and S σ 2 = ∑ i = 1 m ( X i − X ¯ ) 2 , where X ¯ = S μ m {\displaystyle S_{\sigma ^{2}}=\sum _{i=1}^{m}(X_{i}-{\overline {X}})^{2},{\text{ where }}{\overline {X}}={\frac {S_{\mu }}{m}}} is the sample mean, we recognize that T = S... | Wikipedia - Algorithmic inference - The classic solution > Example | 350 | 698 | null |
2 m − 1 ) m / 2 . {\displaystyle f_{T}(t)={\frac {\Gamma (m/2)}{\Gamma ((m-1)/2)}}{\frac {1}{\sqrt {\pi (m-1)}}}\left(1+{\frac {t^{2}}{m-1}}\right)^{m/2}.} Gauging T between two quantiles and inverting its expression as a function of μ {\displaystyle \mu } you obtain confidence intervals for μ {\displaystyle \mu } . Wi... | Wikipedia - Algorithmic inference - The classic solution > Example | 336 | 742 | null |
Section: Inferring functions with the help of a computer. From a modeling perspective the entire dispute looks like a chicken-egg dilemma: either fixed data by first and probability distribution of their properties as a consequence, or fixed properties by first and probability distribution of the observed data as a cor... | Wikipedia - Algorithmic inference - Inferring functions with the help of a computer | 349 | 1,839 | null |
Section: Inferring functions with the help of a computer > Example. A sample of 1,000 independent bits is enough to ensure an absolute error of at most 0.081 on the estimation of the parameter p of the underlying Bernoulli variable with a confidence of at least 0.99. The same size cannot guarantee a threshold less than... | Wikipedia - Algorithmic inference - Inferring functions with the help of a computer > Example | 163 | 739 | null |
Section: The general inversion problem solving the Fisher question > Example. You may find the distribution law of the Pareto parameters A and K as an implementation example of the population bootstrap method as in the figure on the left. Implementing the twisting argument method, you get the distribution law F M ( μ )... | Wikipedia - Algorithmic inference - The general inversion problem solving the Fisher question > Example | 281 | 892 | null |
Its expression is: F M ( μ ) = Φ ( m μ − s M σ m ) , {\displaystyle F_{M}(\mu )=\Phi {\left({\frac {m\mu -s_{M}}{\sigma {\sqrt {m}}}}\right)},} shown in the figure on the right, where Φ {\displaystyle \Phi } is the cumulative distribution function of a standard normal distribution. Computing a confidence interval for M... | Wikipedia - Algorithmic inference - The general inversion problem solving the Fisher question > Example | 344 | 1,405 | null |
Article: Anomaly detection. In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined... | Wikipedia - Anomaly detection - Summary | 304 | 1,678 | null |
Section: History > Intrusion detection. The concept of intrusion detection, a critical component of anomaly detection, has evolved significantly over time. Initially, it was a manual process where system administrators would monitor for unusual activities, such as a vacationing user's account being accessed or unexpect... | Wikipedia - Anomaly detection - History > Intrusion detection | 257 | 1,464 | null |
Section: Methods > Neural networks. Replicator neural networks, autoencoders, variational autoencoders, long short-term memory neural networks Bayesian networks Hidden Markov models (HMMs) Minimum Covariance Determinant Deep Learning Convolutional Neural Networks (CNNs): CNNs have shown exceptional performance in the u... | Wikipedia - Anomaly detection - Methods > Neural networks | 344 | 1,728 | null |
Section: Explainable anomaly detection. Many of the methods discussed above only yield an anomaly score prediction, which often can be explained to users as the point being in a region of low data density (or relatively low density compared to the neighbor's densities). In explainable artificial intelligence, the users... | Wikipedia - Anomaly detection - Explainable anomaly detection | 156 | 801 | null |
Section: History. Aporia was founded in 2019 by Liran Hason and Alon Gubkin. In April 2021, the company raised a $5 million seed round for its monitoring platform for ML models. In February 2022, the company closed a Series A round of $25 million for its ML observability platform. Aporia was named by Forbes as the Next... | Wikipedia - Aporia (company) - History | 259 | 1,231 | null |
Section: Mapping function approach > Inverse reinforcement learning approach. Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and ... | Wikipedia - Apprenticeship learning - Mapping function approach > Inverse reinforcement learning approach | 326 | 1,659 | null |
The researchers found evidence that the techniques may be economically scalable to modern systems. Apprenticeship via inverse reinforcement learning (AIRP) was developed by in 2004 Pieter Abbeel, Professor in Berkeley's EECS department, and Andrew Ng, Associate Professor in Stanford University's Computer Science Depart... | Wikipedia - Apprenticeship learning - Mapping function approach > Inverse reinforcement learning approach | 273 | 1,404 | null |
Section: Example. Learning from demonstration is often explained from a perspective that the working Robot-control-system is available and the human-demonstrator is using it. And indeed, if the software works, the Human operator takes the robot-arm, makes a move with it, and the robot will reproduce the action later. F... | Wikipedia - Apprenticeship learning - Example | 320 | 1,476 | null |
This results into a diagram which produces a pattern. In computer animation, the principle is called spline animation. That means, on the x-axis the time is given, for example 0.5 seconds, 1.0 seconds, 1.5 seconds, while on the y-axis is the variable given. In most cases it's the position of an object. In the inverted ... | Wikipedia - Apprenticeship learning - Example | 196 | 868 | null |
Article: Artificial intelligence in hiring. Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. ... | Wikipedia - Artificial intelligence in hiring - Summary | 157 | 867 | null |
Section: Background. Artificial intelligence has fascinated researchers since the term was coined in the mid-1950s. Researchers have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic. Automation follows a ... | Wikipedia - Artificial intelligence in hiring - Background | 344 | 1,931 | null |
Section: Uses > Screeners. Screeners are tests that allow companies to sift through a large applicant pool and extract applicants that have desirable features. Companies commonly screen through the use of questionnaires, coding tests, interviews, and resume analysis. Artificial Intelligence already plays a major role i... | Wikipedia - Artificial intelligence in hiring - Uses > Screeners | 239 | 1,294 | null |
Section: Uses > Recruitment. Recruitment involves the identification of potential applicants and the marketing of positions. AI is commonly utilized in the recruitment process because it can help boost the number of qualified applicants for positions. Companies are able to use AI to target their marketing to applicants... | Wikipedia - Artificial intelligence in hiring - Uses > Recruitment | 321 | 1,679 | null |
Section: Uses > Interviews. Chatbots were one of the first applications of AI and are commonly used in the hiring process. Interviewees interact with chatbots to answer interview questions, and their responses can then be analyzed by AI, providing prospective employers with a myriad of insights. Chatbots streamline the... | Wikipedia - Artificial intelligence in hiring - Uses > Interviews | 152 | 871 | null |
Section: Controversies. Artificial intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts. AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. Often companies will use data from their employees to decide what people ... | Wikipedia - Artificial intelligence in hiring - Controversies | 337 | 1,855 | null |
This requires AI to make determinations about candidates with very limited information to go off of. Additionally, many employers do not hire employees frequently and so have limited firm specific data to go off. To combat this, many firms will use algorithms and data from other firms in their industry. AI's reliance o... | Wikipedia - Artificial intelligence in hiring - Controversies | 273 | 1,524 | null |
Section: AI and the future of hiring. Artificial intelligence is changing the recruiting process by gradually replacing routine tasks performed by human recruiters. AI can reduce human involvement in hiring and reduce the human biases that hinder effective hiring decisions. And some platforms such as TalAiro go further... | Wikipedia - Artificial intelligence in hiring - AI and the future of hiring | 341 | 1,823 | null |
A third theory holds that the effect of AI and other technological advances is too complicated to yet be understood. This theory is centered around the idea that while AI will likely eliminate jobs in the short term it will also likely increase the demand for other jobs. The question then becomes will the new jobs be a... | Wikipedia - Artificial intelligence in hiring - AI and the future of hiring | 332 | 1,815 | null |
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