Resume

Papers

Year Title Authors Read Cite
2020 A Deep Learning Architecture for Emotional Aware Chatbots R.H.Grouls PDF
2020 Apriority is dead: why all statements should be open to revision R.H.Grouls PDF
2020 Quantifying flow with neural synchronisation R.Grouls, F.C.L. Wildenburg PDF
2020 Neurofeedback personalized with artificial intelligence to support personal development:a preliminary study R.Grouls, M.M.Hemker, A. van Gool PDF
2019 Quantifying flow: changing the mathematics underlying neural synchronisation R.Grouls PDF
2019 Exploring the linguistic capacities of bottlenose dolphins R.Grouls, M.M. Hemker PDF

Blog

Learning to be Fair

Have you ever wondered if the algorithms that increasingly govern our lives are truly fair? In this blog post, I review an intriguing paper titled "Learning to Be Fair: A Consequentialist Approach to Equitable Decision-Making," published on arXiv. This paper discusses the challenges and potential pitfalls of designing fair machine learning systems, as well as what they call a consequentialist approach to addressing these issues. With machine learning systems becoming integral to various aspects of our lives, such as banking, criminal justice, and healthcare, it is crucial that we consider the importance of creating fair and equitable algorithms that make decisions without causing unintended harm to vulnerable groups.

When creating fair machine learning systems, designers often focus on achieving parity in error rates across different protected attributes like race and gender. However, some strategies that seem fair on the surface might not account for the downstream effects, potentially causing harm to the very groups they aim to protect.

For example, gender-blind criminal risk assessments might overestimate the risk of female defendants recidivating, resulting in increased detention rates for women. In another case, when allocating resources to help individuals attend appointments, such as going to court, a strategy that prioritizes those with the largest estimated effect per dollar could inadvertently favor individuals who live closer to the courthouse. This would lead to an unfair allocation of resources, as demonstrated in Santa Clara County case described in the paper where the strategy resulted in a higher average spend on white clients ($7.4) compared to Vietnamese clients ($5.38).

To address these issues, a consequentialist framework (CF) for algorithmic fairness has been proposed. This approach focuses on the outcomes of decisions rather than the properties of the prediction. It begins by identifying the utility of different possible outcomes, such as efficiency and equity, and uses linear programming that incorporates stakeholder preferences to derive optimal decision policies. This method offers advantages over static experimental designs, like randomized control trials, and adapts better to specific scenarios.

The paper on this topic highlights that "using adaptive experimental designs with our framework yields better outcomes for participants during learning, and often more quickly identifies higher utility decision policies for future use, compared to static experimental approaches like randomized control trials." This finding has significant implications for those who are concerned with fairness in machine learning systems.

In conclusion, the paper suggests that causal definitions of algorithmic fairness lead to Pareto-dominated policies. In simpler terms, this means that regardless of one's preference for efficiency or equity, there will always be another strategy that is more satisfying for everyone involved. Hence, it is crucial for designers of machine learning systems to adopt a consequentialist approach to ensure fair and equitable decision-making that benefits all stakeholders.

The boltzmann brain

The Boltzmann brain hypothesis posits that

the spontaneous formation of a solitary brain with fabricated memories in a void is more likely than the universe forming as cosmologists suggest.

Although this idea is absurd, it can serve as a 'reductio ad absurdum' argument, highlighting the limits of our reasoning.The argument exposes a broader issue in our approach to scientific conclusions. We often accept findings that align with our personal belief systems, especially if they are supported by robust statistical evidence. However, when confronted with conclusions that appear absurd, we tend to dismiss the statistics as irrelevant. This reveals a fundamental flaw in our reasoning process: we selectively employ science as a "trustworthy sidekick" until it no longer suits our preferences.

In our public discourse, there are numerous subjects where people quickly jump to conclusions. Questions where there is some evidence, but not enough to be definitive. Some people might feel comfortable with one conclusion, others with another. What should we follow? Our gutfeeling? Statistics? I want to suggest another option, the one where we are comfortable with not knowing for sure.

The scientific method still stands out as the only belief system where changing your belief is considered a good thing. But it can also give us a false sense of grip on our reality when we should actually maintain an open mind.

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