There is a lack of consensus within the literature as to how `fairness' of algorithmic systems can be measured, and different metrics can often be at odds. In this paper, we approach this task by drawing on the ethical frameworks of utilitarianism and John Rawls. Informally, these two theories of distributive justice measure the `good' as either a population's sum of utility, or worst-off outcomes, respectively. We present a parameterized class of objective functions that interpolates between these two (possibly) conflicting notions of the `good'. This class is shown to represent a relaxation of the Rawlsian `veil of ignorance', and its sequence of optimal solutions converges to both a utilitarian and Rawlsian optimum. Several other properties of this class are studied, including: 1) a relationship to regularized optimization, 2) feasibility of consistent estimation, and 3) algorithmic cost. In several real-world datasets, we compute optimal solutions and construct the tradeoff between utilitarian and Rawlsian notions of the `good'. Empirically, we demonstrate that increasing model complexity can manifest strict improvements to both measures of the `good'. This work suggests that the proper degree of `fairness' can be informed by a designer's preferences over the space of induced utilitarian and Rawlsian `good'.

Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.

Language is an important form of data in politics. Constituents express their stances and needs in text such as social media and survey responses. Politicians conduct campaigns through debates, statements of policy positions, and social media. Government staff needs to compile information from various documents to assist in decision-making. Textual data is also prevalent through the documents and debates in the legislation process, negotiations and treaties to resolve international conflicts, and media such as news reports, social media, party platforms, and manifestos. Natural language processing (NLP) is the study of computational methods to automatically analyze text and extract meaningful information for subsequent analysis. The importance of NLP for policymaking has been highlighted since the last century (Gigley, 1993).

The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds. For this, data deletion needs to be deep and permanent, and should be removed from machine learning models. Researchers have proposed machine unlearning algorithms which aim to erase specific data from trained models more efficiently. However, these methods modify how data is fed into the model and how training is done, which may subsequently compromise AI ethics from the fairness perspective. To help software engineers make responsible decisions when adopting these unlearning methods, we present the first study on machine unlearning methods to reveal their fairness implications. We designed and conducted experiments on two typical machine unlearning methods (SISA and AmnesiacML) along with a retraining method (ORTR) as baseline using three fairness datasets under three different deletion strategies. Experimental results show that under non-uniform data deletion, SISA leads to better fairness compared with ORTR and AmnesiacML, while initial training and uniform data deletion do not necessarily affect the fairness of all three methods. These findings have exposed an important research problem in software engineering, and can help practitioners better understand the potential trade-offs on fairness when considering solutions for RTBF.

Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The current GradArgmax ignores the "long-term" benefit of the perturbation. It is faced with zero-gradient and invalid benefit estimates in certain situations. (2) In the reinforcement learning-based attack methods, the learned attack strategies might not be transferable when the attack budget changes. To this end, we first formulate the perturbation space and propose an evaluation framework and the projective ranking method. We aim to learn a powerful attack strategy then adapt it as little as possible to generate adversarial samples under dynamic budget settings. In our method, based on mutual information, we rank and assess the attack benefits of each perturbation for an effective attack strategy. By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes. In the comparative assessment with GradArgmax and RL-S2V, the results show our method owns high attack performance and effective transferability. The visualization of our method also reveals various attack patterns in the generation of adversarial samples.

Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importation of these ideas, combined with an existing rich body of work at the nexus of quantum theory and symmetry, has given rise to the field of Geometric Quantum Machine Learning (GQML). Following the success of its classical counterpart, it is reasonable to expect that GQML will play a crucial role in developing problem-specific and quantum-aware models capable of achieving a computational advantage. Despite the simplicity of the main idea of GQML -- create architectures respecting the symmetries of the data -- its practical implementation requires a significant amount of knowledge of group representation theory. We present an introduction to representation theory tools from the optics of quantum learning, driven by key examples involving discrete and continuous groups. These examples are sewn together by an exposition outlining the formal capture of GQML symmetries via "label invariance under the action of a group representation", a brief (but rigorous) tour through finite and compact Lie group representation theory, a reexamination of ubiquitous tools like Haar integration and twirling, and an overview of some successful strategies for detecting symmetries.

Sound clips of Donald Trump reading the'Three Little Pigs' nursery rhyme aloud and Tom Hanks reciting Pulp Fiction's'Ezekiel 25:17' may sound realistic, but they were generated by artificial intelligence. A developer created a tool, dubbed Tortoise TTS (Text-to-Speech), capable of replicating a person's voice after analyzing 20 seconds of an audio clip with them speaking. Shashank Jain, the creator of Tortoise TTS, said his main idea was to create a tool that allows us to generate podcasts based on text. 'With the arrival of ChatGPT, we can generate conversations in the format we want, provide the feed to the tool I created and outcomes a podcast between two speakers of our choice,' he told DailyMail.com. The sound clips were created with a text-to-speech AI developed by Shashank Jain, who said it was designed to generate podcasts.

BRYAN, Texas, Jan. 05, 2023 (GLOBE NEWSWIRE) -- iBio, Inc. (NYSEA:IBIO) ("iBio" or the "Company"), an AI-driven innovator of precision antibody immunotherapies, today announced that the United States Patent and Trademark Office has issued U.S. Patent No. 11,545,238, entitled "Machine Learning Method For Protein Modelling To Design Engineered Peptides," which covers a machine learning model developed to design engineered epitopes which allow precise steering of therapeutic antibodies towards specific regions of a target protein. "We are thrilled to be issued this U.S. patent, which solidifies our position as a leader in AI-driven drug discovery, and whose claims guarantee broad coverage of our proprietary, epitope-steering antibody discovery engine," said Martin Brenner, DVM. "In addition to marking an important milestone as we transform iBio into an AI-powered biotech company, this patent provides us with a competitive advantage as we continue to build our differentiated pipeline, with benefits that extend to our potential future partners." It uses a combination of a proprietary epitope steering technology, a specialized antibody library, and AI-powered antibody optimization to quickly identify and optimize molecules that can effectively address challenging drug targets. This allows for faster discovery compared to traditional antibody discovery methods.

After years of building experience, developing your knowledge, and honing your skills, you are finally ready to apply for your dream job. But by the time you find out there's an opening and gather your application materials, the position has been filled. The company had recruited candidates using targeted ads on social media and career-oriented websites--ads that you never saw for reasons that are unclear to you. You then apply to another employer, where a human recruiter is impressed by your resume and advances you to the interview stage. But this time, you're rejected after an awkward recorded video interview in which you answered questions read by a computer.

It doesn't take much to get ChatGPT to make a factual mistake. My son is doing a report on U.S. presidents, so I figured I'd help him out by looking up a few biographies. Garry Wills famously wrote "Lincoln at Gettysburg," and Lincoln himself wrote the Emancipation Proclamation, of course, but it's not a bad start. Then I tried something harder, asking instead about the much more obscure William Henry Harrison, and it gamely provided a list, nearly all of which was wrong. Books about Harrison, fewer than half of which are correct.