The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.

Empirical evidence suggests that algorithmic decisions driven by Machine Learning (ML) techniques threaten to discriminate against legally protected groups or create new sources of unfairness. This work supports the contextual approach to fairness in EU non-discrimination legal framework and aims at assessing up to what point we can assure legal fairness through fairness metrics and under fairness constraints. For that, we analyze the legal notion of non-discrimination and differential treatment with the fairness definition Demographic Parity (DP) through Conditional Demographic Disparity (CDD). We train and compare different classifiers with fairness constraints to assess whether it is possible to reduce bias in the prediction while enabling the contextual approach to judicial interpretation practiced under EU non-discrimination laws. Our experimental results on three scenarios show that the in-processing bias mitigation algorithm leads to different performances in each of them. Our experiments and analysis suggest that AI-assisted decision-making can be fair from a legal perspective depending on the case at hand and the legal justification. These preliminary results encourage future work which will involve further case studies, metrics, and fairness notions.

Commercial AI solutions provide analysts and managers with data-driven business intelligence for a wide range of decisions, such as demand forecasting and pricing. However, human analysts may have their own insights and experiences about the decision-making that is at odds with the algorithmic recommendation. In view of such a conflict, we provide a general analytical framework to study the augmentation of algorithmic decisions with human knowledge: the analyst uses the knowledge to set a guardrail by which the algorithmic decision is clipped if the algorithmic output is out of bound, and seems unreasonable. We study the conditions under which the augmentation is beneficial relative to the raw algorithmic decision. We show that when the algorithmic decision is asymptotically optimal with large data, the non-data-driven human guardrail usually provides no benefit. However, we point out three common pitfalls of the algorithmic decision: (1) lack of domain knowledge, such as the market competition, (2) model misspecification, and (3) data contamination. In these cases, even with sufficient data, the augmentation from human knowledge can still improve the performance of the algorithmic decision.

For more than a year now, organizations have struggled to hold onto talent. According to the U.S. Bureau of Labor Statistics, 4.2 million people voluntarily quit their jobs in August 2022. At the same time, there were 10.1 million job openings. Between the Great Resignation and more recent trends like "quiet quitting," traditional approaches for winning talented workers haven't always cut it in this fiercely competitive market. An emerging wave of AI tools for talent management have the potential to help organizations find better job candidates faster, provide more impactful employee development, and promote retention through more effective employee engagement. But while AI might enable leaders to address talent management pain points by making processes faster and more efficient, AI implementation comes with a unique set of challenges that warrant significant attention.

Algorithmic Decision Making (ADM) has permeated all aspects of society. Government organizations are also affected by this trend. However, the use of ADM has been getting negative attention from the public, media, and interest groups. There is little to no actionable guidelines for government organizations to create positive impact through ADM. In this case study, we examined eight municipal organizations in the Netherlands regarding their actual and intended use of ADM. We interviewed key personnel and decision makers. Our results show that municipalities mostly use ADM in an ad hoc manner, and they have not systematically defined or institutionalized a data science process yet. They operate risk averse, and they clearly express the need for cooperation, guidance, and even supervision at the national level. Third parties, mostly commercial, are often involved in the ADM development lifecycle, without systematic governance. Communication on the use of ADM is generally responsive to negative attention from the media and public. There are strong indications for the need of an ADM governance framework. In this paper, we present our findings in detail, along with actionable insights on governance, communication, and performance evaluation of ADM systems.

We consider two fundamental and related issues currently faced by Artificial Intelligence (AI) development: the lack of ethics and interpretability of AI decisions. Can interpretable AI decisions help to address ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education level of the person to whom the explication is intended for. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations do not seem to be enough to address ethical issues. We then propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations. These two opposite paths will play a major role on the future development of ethical AI.

Twitter said Wednesday it was launching an initiative on "responsible machine learning" that will include reviews of algorithmic fairness on the social media platform. The California messaging service said the plan aims to offer more transparency in its artificial intelligence and tackle "the potential harmful effects of algorithmic decisions." The move comes amid heightened concerns over algorithms used by online services, which some say can promote violence or extremist content or reinforce racial or gender bias. "Responsible technological use includes studying the effects it can have over time," said a blog post by Jutta Williams and Rumman Chowdhury of Twitter's ethics and transparency team. "When Twitter uses (machine learning), it can impact hundreds of millions of tweets per day and sometimes, the way a system was designed to help could start to behave differently than was intended."

Algorithms are the heartbeat of applications, but they may not be perceived as entirely benign by their intended beneficiaries. Most educated people know that an algorithm is simply any stepwise computational procedure. Most computer programs are algorithms of one sort of another. Embedded in operational applications, algorithms make decisions, take actions, and deliver results continuously, reliably, and invisibly. But on the odd occasion that an algorithm stings -- encroaching on customer privacy, refusing them a home loan, or perhaps targeting them with a barrage of objectionable solicitation -- stakeholders' understandable reaction may be to swat back in anger, and possibly with legal action.

Measures of algorithmic fairness often do not account for human perceptions of fairness that can substantially vary between different sociodemographics and stakeholders. The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification. It supports (i) studying human perceptions of fairness and (ii) comparing these human perceptions with measures of algorithmic fairness. The framework includes fairness scenario generation, fairness perception elicitation and fairness perception analysis. We demonstrate the FairCeptron framework by applying it to a hypothetical university admission context where we collect human perceptions of fairness in the presence of minorities. An implementation of the FairCeptron framework is openly available, and it can easily be adapted to study perceptions of algorithmic fairness in other application contexts. We hope our work paves the way towards elevating the role of studies of human fairness perceptions in the process of designing algorithmic decision making systems.

In this paper, I argue that counterfactual fairness does not constitute a necessary condition for an algorithm to be fair, and subsequently suggest how the constraint can be modified in order to remedy this shortcoming. To this end, I discuss a hypothetical scenario in which counterfactual fairness and an intuitive judgment of fairness come apart. Then, I turn to the question how the concept of discrimination can be explicated in order to examine the shortcomings of counterfactual fairness as a necessary condition of algorithmic fairness in more detail. I then incorporate the insights of this analysis into a novel fairness constraint, causal relevance fairness, which is a modification of the counterfactual fairness constraint that seems to circumvent its shortcomings.