A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of sta...

https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-042720-125902

Algorithmic Fairness: Choices, Assumptions, and Definitions

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks. The article begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process, and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, toward a better understanding of which mechanisms should be used in different scenarios. The article ends by reviewing several emerging research sub-fields of algorithmic fairness, beyond classification.

A Review on Fairness in Machine Learning

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.

/pdf/decolonial-ai-decolonial-theory-as-sociotechnical-foresight-3gseanit6e.pdf

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles --- under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions

Moral Machines: Teaching Robots Right from Wrong

We revisit the notion of individual fairness first proposed by Dwork et al. [2012], which asks that "similar individuals should be treated similarly". A primary difficulty with this definition is that it assumes a completely specified fairness metric for the task at hand. In contrast, we consider a framework for fairness elicitation, in which fairness is indirectly specified only via a sample of pairs of individuals who should be treated (approximately) equally on the task. We make no assumption that these pairs are consistent with any metric. We provide a provably convergent oracle-efficient algorithm for minimizing error subject to the fairness constraints, and prove generalization theorems for both accuracy and fairness. Since the constrained pairs could be elicited either from a panel of judges, or from particular individuals, our framework provides a means for algorithmically enforcing subjective notions of fairness. We report on preliminary findings of a behavioral study of subjective fairness using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset.

pdf/eliciting-and-enforcing-subjective-individual-fairness-57cf2lo112.pdf

Eliciting and Enforcing Subjective Individual Fairness.

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders. We introduce a framework in which pairs of individuals can be identified as requiring (approximately) equal treatment under a learned model, or requiring ordered treatment such as "applicant Alice should be at least as likely to receive a loan as applicant Bob". We provide a provably convergent and oracle efficient algorithm for learning the most accurate model subject to the elicited fairness constraints, and prove generalization bounds for both accuracy and fairness. This algorithm can also combine the elicited constraints with traditional statistical fairness notions, thus "correcting" or modifying the latter by the former. We report preliminary findings of a behavioral study of our framework using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset.

An Algorithmic Framework for Fairness Elicitation

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders' nuanced viewpoints in real-world contexts To address this gap, we propose a framework for eliciting stakeholders' subjective fairness notions Combining a user interface that allows stakeholders to examine the data and the algorithm's predictions with an interview protocol to probe stakeholders' thoughts while they are interacting with the interface, we can identify stakeholders' fairness beliefs and principles We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints We also discuss how our results can inform the design of predictive systems

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems

Nature has ended. Acid rain and global warming leave no place untouched by human hands. We can no longer think of 'the environment' as synonymous with 'nature'. Instead, Steven Vogel argues that the environment is more like a mall: it is built. And because we build the environment, we are responsible for it. Yet, other things build, too. Animals build and use tools. Machines and algorithms build everything from skyscrapers to cell phones. Are they responsible for what they build? While animals and robots are normally considered in distinct philosophical fields, Vogel’s rejection of the natural-artificial split prompts us to question the distinction between natural and artificial agents. I argue, under consistent reasons, that neither animals nor robots are morally responsible for what they do. When machines act in morally consequential ways, then, we cannot blame the robot. However, we usually think to blame those who built the robot. I present a theory of how a builder may be responsible for what they build. Then, I argue that there are cases where neither the robot nor the engineer can be blamed for the robot's actions. Drawing on Vogel, Karl Marx, and Martin Heidegger, I explore moral and environmental responsibility through meditations on animals and machines.

/pdf/animals-machines-and-moral-responsibility-in-a-built-3qe3zbphn3.pdf

Animals, Machines, and Moral Responsibility in a Built Environment

We study a multi-armed bandit learning setting where a social planner incentivizes a set of heterogeneous agents to efficiently explore the set of available arms. At each round, an agent arrives with their unobserved private type that determines both their prior preferences across the actions as well as their action-independent confounding shift in the rewards. The planner provides the agent with an arm recommendation that may alter their belief and incentivize them to explore potentially sub-optimal arms. Under this setting, we provide a novel recommendation mechanism that views the planner’s recommendations as a form of instrumental variables (IV) that only affect agents’ arm selection but not the observed rewards. We construct such IVs by carefully mapping the history–the interactions between the planner and the previous agents–to a random arm recommendation. Despite the unobserved confounding shift in the rewards, the resulting IV regression provides reliable estimates on the mean rewards of the actions and enables the social learning process to minimize regret over the long term.

Incentivizing Bandit Exploration: Recommendations as Instruments

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