Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to 'algorithmic positive action' under European Union (EU) non-discrimination law. As the Court of Justice of the European Union has been strict when it comes to assessing the lawfulness of positive action, this would impose a significant legal burden on those wishing to implement fair-ml interventions. In this paper, we propose that algorithmic fairness interventions often should be interpreted as a means to prevent discrimination, rather than a measure of positive action. Specifically, we suggest that this category mistake can often be attributed to neutrality fallacies: faulty assumptions regarding the neutrality of fairness-aware algorithmic decision-making. Our findings raise the question of whether a negative obligation to refrain from discrimination is sufficient in the context of algorithmic decision-making. Consequently, we suggest moving away from a duty to 'not do harm' towards a positive obligation to actively 'do no harm' as a more adequate framework for algorithmic decision-making and fair ml-interventions.

Algorithmic decision-making is increasingly being adopted across public higher education. The expansion of data-driven practices by post-secondary institutions has occurred in parallel with the adoption of New Public Management approaches by neoliberal administrations. In this study, we conduct a qualitative analysis of an in-depth ethnographic case study of data and algorithms in use at a public college in Ontario, Canada. We identify the data, algorithms, and outcomes in use at the college. We assess how the college's processes and relationships support those outcomes and the different stakeholders' perceptions of the college's data-driven systems. In addition, we find that the growing reliance on algorithmic decisions leads to increased student surveillance, exacerbation of existing inequities, and the automation of the faculty-student relationship. Finally, we identify a cycle of increased institutional power perpetuated by algorithmic decision-making, and driven by a push towards financial sustainability.

Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons. To achieve this, prior research has contributed various approaches that ensure fairness in machine learning predictions, while comparatively little effort has focused on fairness in decision-making, specifically off-policy learning. In this paper, we propose a novel framework for fair off-policy learning: we learn decision rules from observational data under different notions of fairness, where we explicitly assume that observational data were collected under a different - potentially discriminatory - behavioral policy. We then propose a neural networkbased framework to learn optimal policies under different fairness notions. We further provide theoretical guarantees in the form of generalization bounds for the finite-sample version of our framework. We demonstrate the effectiveness of our framework through extensive numerical experiments using both simulated and real-world data. Altogether, our work enables algorithmic decision-making in a wide array of practical applications where fairness must be ensured. Algorithmic decision-making in practice must avoid discrimination and thus be fair to meet legal, ethical, and societal demands (Nkonde, 2019; De-Arteaga et al., 2022; Corbett-Davies et al., 2023). For example, in the U.S., the Fair Housing Act and Equal Credit Opportunity Act stipulate that decisions must not be subject to systematic discrimination by gender, race, or other attributes deemed as sensitive. However, research from different areas has provided repeated evidence that algorithmic decisionmaking is often not fair. A prominent example is Amazon's tool for automatically screening job applicants that was used between 2014 and 2017 (Dastin, 2018). It was later discovered that the underlying algorithm generated decisions that were subject to systematic discrimination against women and thus resulted in a ceteris paribus lower probability of women being hired. Ensuring fairness in off-policy learning is subject to inherent challenges.

Well-known examples of such "high-risk" [6] systems can be found in recidivism prediction [5], refugee resettlement [3], and public employment [19]. Many authors have outlined that faulty or biased predictions by ADM systems can have far-reaching consequences, including discrimination [5], inaccurate predictions [4], and overreliance on automated decisions [2]. Therefore, high-level guidelines are meant to prevent these issues by pointing out ways to develop trustworthy and ethical AI [10, 22]. However, practically applying these guidelines remains challenging, since the meaning and priority of ethical values shift depending on who is asked [11]. Recent work in Explainable Artificial Intelligence (XAI) thus suggests equipping individuals who are involved with an ADM system and carry responsibility--so-called "stakeholders"--with the means of assessing the system themselves, i.e. enabling users, deployers, and affected individuals to independently check the system's ethical values [14]. Arguably, a pronounced understanding of the system is necessary for making such an assessment. While numerous XAI studies have examined how explaining an ADM system can increase stakeholders' understanding [20, 21], we highlight two aspects that remain an open challenge: i) the amounts of resources needed to produce and test domain-specific explanations and ii) the difficulty of creating and evaluating understanding for a large variety of people. Further, it is important to note that, despite our reference to "Explainable AI," ADM is not constrained to AI, and indeed might encompass a broader problem space. Despite the emphasis on "understanding" in XAI research, the field features only a few studies that introduce learning frameworks from other disciplines.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session (EPIC gave VentureBeat permission to reprint the full roundup below). Within the past year, according to the document (which was compiled by summer clerk Caroline Kraczon), states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session. Within the past year, according to the document, states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

It covers a research field where a wide variety of experts come together: mathematicians, engineers, psychologists, philosophers and regulators, which makes it one of the most interesting. People make noisy, inconsistent decisions based on assumptions that are sometimes hard to check, and so does AI. The European Commission has recently published a proposal for what is going to be the first attempt ever to insert AI systems and their use in a coherent legal framework. The proposal explicitly refers to the risk of AI systems not being explainable and possibly being biased as a result. To me (and the people at Deeploy), explainability of AI means that everyone involved in algorithmic decision-making should be able to understand well enough how decisions have been made.

Explainability is a fascinating topic. It covers a research field where a wide variety of experts come together: mathematicians, engineers, psychologists, philosophers and regulators, which makes it one of the most interesting. I have been involved in quite some AI projects where explainability -- or XAI -- turned out to be crucial. So, I decided to gather and share my experiences, and the experiences of my colleagues at Deeploy. AI is one of the biggest innovations of our time. It can change the way we live, work, care, teach and interact with each other.

Global corporate investments in AI are projected to double to around $110 billion over the next two years. One of the main areas of application for organizational AI is recruitment (e.g., staffing, hiring, employee selection, etc.), not least because human recruiters waste a great deal of time entering data, sorting through resumes, and making imprecise inferences about candidates' talent and potential. Across countries and industries, big firms, such as IKEA, Unilever, Intel and Vodafone rely on algorithmic decision-making in their recruitment processes. It is noteworthy that many of the most popular traditional hiring methods have very low accuracy, and attempts to evaluate other people's job suitability are typically contaminated by the usual stereotypes and prejudices that undermine human objectivity and reduce our ability to understand others. Unsurprisingly, many recruiters see technological innovations, such as machine-learning algorithms, as a big time saver, and emerging academic research suggests that AI can also increase organizations' efforts to accurately predict employee job performance and select the right person for the right role, as well as increasing fairness, transparency, and consistency.

Physicists are increasingly developing artificial intelligence and machine learning techniques to advance our understanding of the physical world but there is a rising concern about the bias in such systems and their wider impact on society at large. In 2011, during her undergraduate degree at Georgia Institute of Technology, Ghanaian-US computer scientist Joy Buolamwini discovered that getting a robot to play a simple game of peek-a-boo with her was impossible – the machine was incapable of seeing her dark-skinned face. Later, in 2015, as a Master's student at Massachusetts Institute of Technology's Media Lab working on a science–art project called Aspire Mirror, she had a similar issue with facial analysis software: it detected her face only when she wore a white mask. Buolamwini's curiosity led her to run one of her profile images across four facial recognition demos, which, she discovered, either couldn't identify a face at all or misgendered her – a bias that she refers to as the "coded gaze". She then decided to test 1270 faces of politicians from three African and three European countries, with different features, skin tones and gender, which became her Master's thesis project "Gender Shades: Intersectional accuracy disparities in commercial gender classification" (figure 1).