We are sceptical of concerns over the opacity of algorithmic decision tools. While
transparency and explainability are certainly important desiderata in algorithmic …

Should decision-making algorithms be held to higher standards of transparency than human
beings? The way we answer this question directly impacts what we demand from …

The ever-increasing application of algorithms to decision-making in a range of social
contexts has prompted demands for algorithmic accountability. Accountable decision …

Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms
of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a …

Many artificial intelligence (AI) systems currently used for decision-making are opaque, ie,
the internal factors that determine their decisions are not fully known to people due to the …

In this paper, we point out that explainability is useful but not sufficient to ensure the
legitimacy of algorithmic decision systems. We argue that the key requirements for high …

Decision-making algorithms are being used in important decisions, such as who should be
enrolled in health care programs and be hired. Even though these systems are currently …

In this paper we argue that transparency of machine learning algorithms, just as explanation,
can be defined at different levels of abstraction. We criticize recent attempts to identify the …

Businesses increasingly rely on algorithms that are data-trained sets of decision rules (ie,
the output of the processes often called “machine learning”) and implement decisions with …

The increasing use of Artificial Intelligence (AI) for making decisions in public affairs has
sparked a lively debate on the benefits and potential harms of self-learning technologies …