Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue t...
Today's increased availability of large amounts of human behavioral data and advances in artificial ...
Computational artificial intelligence (AI) algorithms are increasingly used to support decision-maki...
This research paper explores self-explaining AI models that bridge the gap between complex black-box...
Advancements in machine learning have fuelled the popularity of using AI decision algorithms in proc...
The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed...
We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and ...
The world is currently experiencing an extraordinary explosion of data due to the advancements in di...
Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the i...
The requirements of transparency or explainability draw considerable attention in AI ethics. Still, ...
For several years, scholars have (for good reason) been largely preoccupied with worries about the u...
Artificial Intelligence (AI) has rapidly become an integral part of decision-making processes across...
In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethi...
Sofia Olhede and Russell Rodrigues discuss recent efforts to ensure greater scrutiny of machine-gene...
The combination of increased availability of large amounts of fine-grained human behavioral data and...
Decision-making algorithms are being used in important decisions, such as who should be enrolled in ...
Today's increased availability of large amounts of human behavioral data and advances in artificial ...
Computational artificial intelligence (AI) algorithms are increasingly used to support decision-maki...
This research paper explores self-explaining AI models that bridge the gap between complex black-box...
Advancements in machine learning have fuelled the popularity of using AI decision algorithms in proc...
The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed...
We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and ...
The world is currently experiencing an extraordinary explosion of data due to the advancements in di...
Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the i...
The requirements of transparency or explainability draw considerable attention in AI ethics. Still, ...
For several years, scholars have (for good reason) been largely preoccupied with worries about the u...
Artificial Intelligence (AI) has rapidly become an integral part of decision-making processes across...
In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethi...
Sofia Olhede and Russell Rodrigues discuss recent efforts to ensure greater scrutiny of machine-gene...
The combination of increased availability of large amounts of fine-grained human behavioral data and...
Decision-making algorithms are being used in important decisions, such as who should be enrolled in ...
Today's increased availability of large amounts of human behavioral data and advances in artificial ...
Computational artificial intelligence (AI) algorithms are increasingly used to support decision-maki...
This research paper explores self-explaining AI models that bridge the gap between complex black-box...