Discrimination, artificial intelligence, and algorithmic decision-making external link

vol. 2019, 2019

Abstract

This report, written for the Anti-discrimination department of the Council of Europe, concerns discrimination caused by algorithmic decision-making and other types of artificial intelligence (AI). AI advances important goals, such as efficiency, health and economic growth but it can also have discriminatory effects, for instance when AI systems learn from biased human decisions. In the public and the private sector, organisations can take AI-driven decisions with farreaching effects for people. Public sector bodies can use AI for predictive policing for example, or for making decisions on eligibility for pension payments, housing assistance or unemployment benefits. In the private sector, AI can be used to select job applicants, and banks can use AI to decide whether to grant individual consumers credit and set interest rates for them. Moreover, many small decisions, taken together, can have large effects. By way of illustration, AI-driven price discrimination could lead to certain groups in society consistently paying more. The most relevant legal tools to mitigate the risks of AI-driven discrimination are nondiscrimination law and data protection law. If effectively enforced, both these legal tools could help to fight illegal discrimination. Council of Europe member States, human rights monitoring bodies, such as the European Commission against Racism and Intolerance, and Equality Bodies should aim for better enforcement of current nondiscrimination norms. But AI also opens the way for new types of unfair differentiation (some might say discrimination) that escape current laws. Most non-discrimination statutes apply only to discrimination on the basis of protected characteristics, such as skin colour. Such statutes do not apply if an AI system invents new classes, which do not correlate with protected characteristics, to differentiate between people. Such differentiation could still be unfair, however, for instance when it reinforces social inequality. We probably need additional regulation to protect fairness and human rights in the area of AI. But regulating AI in general is not the right approach, as the use of AI systems is too varied for one set of rules. In different sectors, different values are at stake, and different problems arise. Therefore, sector-specific rules should be considered. More research and debate are needed.

Artificial intelligence, discriminatie, frontpage, kunstmatige intelligentie, Mensenrechten

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Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem external link

Philosophical Transactions of the Royal Society A, vol. 376, num: 2135, pp: 1-21, 2018

Abstract

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision.

access to information, algoritmes, Artificial intelligence, frontpage, news, persona, Personalisation, right to receive information, user agency

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Automated Decision-Making Fairness in an AI-driven World: Public Perceptions, Hopes and Concerns external link

Araujo, T., Vreese, C.H. de, Helberger, N., Kruikemeier, S., Weert, J. van,, Bol, N., Oberski, D., Pechenizkiy, M., Schaap, G. & Taylor, L.
2018

Abstract

Ongoing advances in artificial intelligence (AI) are increasingly part of scientific efforts as well as the public debate and the media agenda, raising hopes and concerns about the impact of automated decision making across different sectors of our society. This topic is receiving increasing attention at both national and cross- national levels. The present report contributes to informing this public debate, providing the results of a survey with 958 participants recruited from high-quality sample of the Dutch population. It provides an overview of public knowledge, perceptions, hopes and concerns about the adoption of AI and ADM across different societal sectors in the Netherlands. This report is part of a research collaboration between the Universities of Amsterdam, Tilburg, Radboud, Utrecht and Eindhoven (TU/e) on automated decision making, and forms input to the groups’ research on fairness in automated decision making.

algoritmes, Artificial intelligence, automated decision making, frontpage

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Before the Singularity: Copyright and the Challenges of Artificial Intelligence external link

González Otero, B., & Quintais, J.
Kluwer Copyright Blog, vol. 2018, 2018

Artificial intelligence, Copyright, frontpage

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