Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

Research programs in empirical psychology over the past few decades have led scholars to posit implicit biases. This is due to the development of innovative behavioural measures that have revealed aspects of our cognitions which may not be identified on self-report measures requiring individuals to reflect on and report their attitudes and beliefs. But what does it mean to characterise such biases as implicit? Can we satisfactorily articulate the grounds for identifying them as bias? And crucially, what sorts of cognitions (...) are in fact being measured; what mental states or processes underpin such behavioural responses? In this paper, we outline some of the philosophical and empirical issues engaged when attempting to address these three questions. Our aim is to provide a constructive taxonomy of the issues, and how they interrelate. As we will see, any view about what implicit bias is may depend on a range of prior theoretical choices. (shrink)

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we (...) provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. (shrink)

Perhaps this is due to the belief that technology has a value-neutral nature, and that issues of value are better left to philosophers.

The paper puts forward a pluralistic account of fairness within which concepts of equality of sacrifice and outcome, desert, and randomized outcomes within a fair framework all have their place. The distinction between efficiency and fairness is set out early on, and it is later argued that only efficient social arrangements can withstand the questioning about the fairness of the way they distribute their benefits to their beneficiaries and impose demands on those whose taxes pay for them that the modern (...) welfare state increasingly has to face. (shrink)

A major international consultancy firm identified ‘AI ethicist’ as an essential position for companies to successfully implement artificial intelligence (AI) at the start of 2019. It declares that AI ethicists are needed to help companies navigate the ethical and social issues raised by the use of AI. Top 5 AI hires companies need to succeed in 2019. The view that AI is beneficial but nonetheless potentially harmful to individuals and society is widely shared by the industry, academia, governments, and civil (...) society organizations. Accordingly and in order to realize its benefits while avoiding ethical pitfalls and harmful consequences, numerous initiatives have been established to a) examine the ethical, social, legal and political dimensions of AI and b) develop ethical guidelines and recommendations for design and implementation of AI. (shrink)