Human decisions are also difficult to probe or review: people may lie about the factors they considered, or may not understand the factors that influenced their thinking, leaving room for unconscious bias. CDEI launches a ‘roadmap’ for tackling algorithmic bias A review from the Centre for Data Ethics and Innovation (CDEI) has led to the creation of a “roadmap” for tackling algorithmic bias. The growing use of artificial intelligence in sensitive areas, including for hiring, criminal justice, and healthcare, has stirred a debate about bias and fairness. … On one hand, AI can help reduce the impact of human biases in decisionmaking. It's a type of software that can speed up decision-making, and grow more useful with more data. Tackling Bias Issues in Artificial Intelligence – Lexology. These guidelines prescribe seven key requirements that the AI systems should meet. Published Date: 12. Those striving to maximize fairness and minimize bias from AI could consider several paths forward: When deploying AI, it is important to anticipate domains potentially prone to unfair bias, such as those with previous examples of biased systems or with skewed data. July 23, 2018 | Updated: July 24, 2018 . It’s used to make diagnostic decisions in healthcare, to allocate resources for social services in things like child protection, to help recruiters crunch through piles of job applications, and much more. Tackling unfair bias will require drawing on a portfolio of tools and procedures. Tackling bias in artificial intelligence. How should we codify definitions of fairness? AI News September 11, 2020 . This latter group includes “counterfactual fairness” approaches, which are based on the idea that a decision should remain the same in a counterfactual world in which a sensitive attribute is changed. Some researchers have highlighted how judges’ decisions can be unconsciously influenced by their own personal characteristics, while employers have been shown to grant interviews at different rates to candidates with identical resumes but with names considered to reflect different racial groups. Published Date: 11. Currently, there is a lot of debate around tackling bias in artificial intelligence. Algorithmic bias has become a hot topic in recent months and as AI becomes more widely used the subject is becoming ever more important. Be aware of the contexts in which AI can help correct for bias as well as where there is a high risk that AI could exacerbate bias. However, reliance on AI also carries risks, especially where decisions are made without human oversight. Perhaps organizations can benefit from the recent progress made on measuring fairness by applying the most relevant tests for bias to human decisions, too. AI systems learn to … is presented as a potent, pervasive, unstoppable force to solve our biggest problems, even though it’s essentially just about finding patterns in vast quantities of data. What can business and policy leaders do to minimize bias in AI going forward? Among others, here are six steps that companies should consider. Tackling Unconscious Bias with Artificial Intelligence. Machine learning relies on pattern-recognition within datasets. AI can help humans with bias — but only if humans are working together to tackle bias in AI. September 2020. Six potential ways forward for AI practitioners and business and policy leaders to consider 1. Published Date: 11. Similarly, if an organization realizes an algorithm trained on its human decisions (or data based on prior human decisions) shows bias, it should not simply cease using the algorithm but should consider how the underlying human behaviors need to change. Discussion Paper - McKinsey Global Institute. Bias points in AI decisionmaking have change into more and more problematic in recent times, as many firms enhance using AI methods throughout their operations. Recently, a technology company discontinued development of a hiring algorithm based on analyzing previous decisions after discovering that the algorithm penalized applicants from women’s colleges. The first consists of pre-processing the data to maintain as much accuracy as possible while reducing any relationship between outcomes and protected characteristics, or to produce representations of the data that do not contain information about sensitive attributes. Bias issues in AI decisionmaking have become increasingly problematic in recent years, as many companies increase the use of AI systems across their operations. Learn more about cookies, Opens in new Sweeney hypothesized that even if different versions of the ad copy—versions with and without “arrest”—were initially displayed equally, users may have clicked on different versions more frequently for different searches, leading the algorithm to display them more often. It has gone to the point that it is used in riskier areas such as hiring, criminal justice, and healthcare. For example, Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, as well as Alexandra Chouldechova and others, have demonstrated that a model cannot conform to more than a few group fairness metrics at the same time, except under very specific conditions. Operational strategies can include improving data collection through more cognizant sampling and using internal “red teams” or third parties to audit data and models. If you would like information about this content we will be happy to work with you. 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