The Intuitive Appeal of Explainable Machines

Abstract

As algorithmic decision-making has become synonymous with inexplicable decision-making, we have become obsessed with opening the black box. This Article responds to a growing chorus of legal scholars and policymakers demanding explainable machines. Their instinct makes sense; what is unexplainable is usually unaccountable. But the calls for explanation are a reaction to two distinct but often conflated properties of machine-learning models: inscrutability and non intuitiveness. Inscrutability makes one unable to fully grasp the model, while non intuitiveness means one cannot understand why the model’s rules are what they are. Solving inscrutability alone will not resolve law and policy concerns; accountability relates not merely to how models work, but whether they are justified.

In this Article, we first explain what makes models inscrutable as a technical matter. We then explore two important examples of existing regulation-by-explanation and techniques within machine learning for explaining inscrutable decisions. We show that while these techniques might allow machine learning to comply with existing laws, compliance will rarely be enough to assess whether decision-making rests on a justifiable basis.

We argue that calls for explainable machines have failed to recognize the connection between intuition and evaluation and the limitations of such an approach. A belief in the value of explanation for justification assumes that if only a model is explained, problems will reveal themselves intuitively. Machine learning, however, can uncover relationships that are both non-intuitive and legitimate, frustrating this mode of normative assessment. If justification requires understanding why the model’s rules are what they are, we should seek explanations of the process behind a model’s development and use, not just explanations of the model itself. This Article illuminates the explanation-intuition dynamic and offers documentation as an alternative approach to evaluating machine learning models.

Full abstract and research here:

http://blog.experientia.com/paper-intuitive-appeal-explainable-machines/

Algorithms, Artificial Intelligence, Accountability of AI, Explanation, ResearchBrigitte BellanMarch 3, 2018Explanation, Yale, Cornell University, Andrew D. Selbst, Solon Barocas, machine learning, Black Box

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration.

AI, Algorithms, Artificial Intelligence, Gender Bias, Machine Learning, FairnessBrigitte BellanNovember 20, 2017Algorithmic accountability, algorithms, discrimination, machine learning, personal data, privacy