The hermit and mystic Richard Rolles was basically a bestselling author in the Middle Ages. Richard Rolle (depicted in this medieval illustration c. 1400) was a famous hermit and Christian mystic. Breakthroughs, discoveries, and DIY tips sent six days a week. For generations, a misidentified medieval manuscript was hidden in a 474-year-old English boarding school's library. After a careful new analysis, a medieval literature researcher can confirm the manuscript is actually the oldest and only known edition of Richard Rolle's () written in its original Latin.

Machine learning models are vulnerable to adversarial attacks, including attacks that leak information about the model's training data. There has recently been an increase in interest about how to best address privacy concerns, especially in the presence of data-removal requests. Machine unlearning algorithms aim to efficiently update trained models to comply with data deletion requests while maintaining performance and without having to resort to retraining the model from scratch, a costly endeavor. Several algorithms in the machine unlearning literature demonstrate some level of privacy gains, but they are often evaluated only on rudimentary membership inference attacks, which do not represent realistic threats. In this paper we describe and propose alternative evaluation methods for three key shortcomings in the current evaluation of unlearning algorithms. We show the utility of our alternative evaluations via a series of experiments of state-of-the-art unlearning algorithms on different computer vision datasets, presenting a more detailed picture of the state of the field.

As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development.

Once upon a time, long before smartphones or even laptops were ubiquitous, the computer mouse was new, and it was thrilling. The 1984 Macintosh wasn't the first machine to come with one, but it was the first to popularize the gizmo for ordinary people. Proper use of the mouse was not intuitive. Many people had a hard time moving and clicking at the same time, and "double-clicking" was a skill one had to learn. Still, anyone could put a hand on the thing, move it around on a table, and see the results on-screen: A little cursor moved along with you.

The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. It was a significant emergent right as the result of the evolution of technology. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and prompt engineering. With the rapid advancement of AI and the increasing need of regulating this powerful technology, learning from the case of RTBF can provide valuable lessons for technical practitioners, legal experts, organizations, and authorities.

The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an algorithmic decision, the right to be forgotten grants them the right to ask for their data to be deleted from all the databases and models of an organization. Intuitively, enforcing the right to be forgotten may trigger model updates which in turn invalidate previously provided explanations, thus violating the right to explanation. In this work, we investigate the technical implications arising due to the interference between the two aforementioned regulatory principles, and propose the first algorithmic framework to resolve the tension between them. To this end, we formulate a novel optimization problem to generate explanations that are robust to model updates due to the removal of training data instances by data deletion requests. We then derive an efficient approximation algorithm to handle the combinatorial complexity of this optimization problem. We theoretically demonstrate that our method generates explanations that are provably robust to worst-case data deletion requests with bounded costs in case of linear models and certain classes of non-linear models. Extensive experimentation with real-world datasets demonstrates the efficacy of the proposed framework.

As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA). One key principle is the "right to be forgotten" which gives users the right to have their data deleted. Another key principle is the right to an actionable explanation, also known as algorithmic recourse, allowing users to reverse unfavorable decisions. To date it is unknown whether these two principles can be operationalized simultaneously. Therefore, we introduce and study the problem of recourse invalidation in the context of data deletion requests. More specifically, we theoretically and empirically analyze the behavior of popular state-of-the-art algorithms and demonstrate that the recourses generated by these algorithms are likely to be invalidated if a small number of data deletion requests (e.g., 1 or 2) warrant updates of the predictive model.

Data protection and privacy have been discussed nonstop as more and more people come to realize just how much personal information they are sharing through the countless apps and websites they regularly visit. It's no longer so surprising to see products you've talked about with friends or concerts you've searched on Google promptly appear as advertisements in your social media feeds. And that has many people concerned. Recent government initiatives such as the EU's General Data Protection Regulation (GDPR) are designed to protect individuals' data privacy, with a core concept being "the right to be forgotten." The bad news is, it's generally difficult to revoke things that have already been shared online or to properly delete such data.

At the peak of the Japanese harvest, Makoto Koike's mother spends around eight hours a day sorting cucumbers from the family farm into different categories – a dull, time-consuming task that her son decided to automate. Although Makoto wasn't a machine learning expert, he started playing around with TensorFlow, Google's popular open-source machine learning framework, and developed a deep learning model that could sort cucumbers by size, shape and other attributes. The system isn't perfect (it has an accuracy rate of around 75%). But it's a sign of how AI could soon transform even the smallest family-run business. Giants like Google, Amazon, Microsoft, Apple and Facebook are, of course, well-aware of this transformative power.

Artificial intelligence is one of the technologies that'll define the world over the next few years. While this innovation is still in the early stages, it's already demonstrating that it can compete with human intelligence. From writing computer code to beating professional poker players, AI is constantly advancing. In the business world, artificial intelligence can speed up complex, timely processes and improve efficiencies overall. When it comes to using AI systems in the workplace, opinion seems to be varied.