They can be used to decide everything from which video we're recommended to watch next on YouTube to who should be arrested based on facial recognition software. But these algorithms, and the data used to train them, often replicate the harmful social biases of the engineers who build them. Eliminating this bias from technology is the focus of Code Shift, a new data science lab at Texas A&M University that brings together faculty members and researchers from a variety of disciplines across campus. It's an increasingly critical initiative, said Lab Director Srividya Ramasubramanian, as more of the world becomes automated. Machines, rather than humans, are making many of the decisions around us, including some that are high-risk.

Artificial intelligence (AI) is sweeping through industries ranging from cybersecurity to environmental protection -- and the Covid-19 pandemic has only accelerated this trend. AI may improve the lives of millions, but it also will inevitably cause accidents that injure people or parties -- indeed, it already has through incidents like autonomous vehicle crashes. An outdated liability system in the United States and other countries, however, is unable to manage these risks, which is a problem because those risks can impede AI innovations and adoption. Therefore, it is crucial that we reform the liability system. Doing so will help speed AI innovations and adoption.

France has fined Google €500 million ($590 million) in the latest antitrust ruling against the company. Authorities say Google did not reach a fair agreement with publishers to use snippets of their content on Google News, despite a 2020 order for the company to do so. Google and French newspaper group Alliance de la presse d'information générale agreed on a payment framework for news previews in January, and it has been in discussions with Agence France-Presse and magazine publishers. However, regulators said Google's payment offers were "negligible," as Bloomberg reports. Isabelle de Silva, head of competition regulator Autorité de la concurrence, said Google offered to pay less for news than it does for weather data or dictionary definitions.

The technologies referred to as "artificial intelligence" or "AI" are more momentous than most people realize. Their impact will be at least equal to, and may well exceed, that of electricity, the computer, and the internet. What's more, their impact will be massive and rapid, faster than what the internet has wrought in the past thirty years. Much of it will be wondrous, giving sight to the blind and enabling self-driving vehicles, for example, but AI-engendered technology may also devastate job rolls, enable an all- encompassing surveillance state, and provoke social upheavals yet unforeseen. The time we have to understand this fast-moving technology and establish principles for its governance is very short. The term "AI" was coined by a computer scientist in 1956.

Marketing messaging is moving to local brands and locations, as customers want to have relationships with brands on a local level, and collaborative marketing helps local businesses market more effectively. AI and machine learning are delivering on their marketing promise to personalize the customer experience at a speed and scale never seen before, and collaborative marketing lets local marketers leverage these powerful technologies. The rising tide of privacy laws make third-party data more difficult to acquire, including the impending demise of third-party cookies disrupting the established digital advertising ecosystem, and collaborative marketing provides an alternative path. The pandemic has accelerated customers' move to digital channels and boosted demand for audience behavioral data, rich customer insights and testing methodologies, which collaborative marketing facilitates. Marketing messaging is moving to local brands and locations, as customers want to have relationships with brands on a local level, and collaborative marketing helps local businesses market more effectively.

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction. Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task. Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome. Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity. The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956.

Artificial Intelligence has the power to create our dream utopia -- it can eliminate discrimination based on gender, religion, race, sexuality or ethnicity. But the truth is that AI also has the power to worsen discrimination. The data that is fed into AI systems largely impact the results obtained from the systems, and having an unbalanced dataset can lead to racially skewed results.

The ability to convey relevant and diverse information is critical in multi-document summarization and yet remains elusive for neural seq-to-seq models whose outputs are often redundant and fail to correctly cover important details. In this work, we propose an attention mechanism which encourages greater focus on relevance and diversity. Attention weights are computed based on (proportional) probabilities given by Determinantal Point Processes (DPPs) defined on the set of content units to be summarized. DPPs have been successfully used in extractive summarisation, here we use them to select relevant and diverse content for neural abstractive summarisation. We integrate DPP-based attention with various seq-to-seq architectures ranging from CNNs to LSTMs, and Transformers. Experimental evaluation shows that our attention mechanism consistently improves summarization and delivers performance comparable with the state-of-the-art on the MultiNews dataset.

Recently, abstract argumentation-based models of case-based reasoning ($AA{\text -} CBR$ in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios. However, the formal properties of $AA{\text -} CBR$ as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of $AA{\text -} CBR$ (that we call $AA{\text -} CBR_{\succeq}$). Specifically, we prove that $AA{\text -} CBR_{\succeq}$ is not cautiously monotonic, a property frequently considered desirable in the literature. We then define a variation of $AA{\text -} CBR_{\succeq}$ which is cautiously monotonic. Further, we prove that such variation is equivalent to using $AA{\text -} CBR_{\succeq}$ with a restricted casebase consisting of all "surprising" and "sufficient" cases in the original casebase. As a by-product, we prove that this variation of $AA{\text -} CBR_{\succeq}$ is cumulative, rationally monotonic, and empowers a principled treatment of noise in "incoherent" casebases. Finally, we illustrate $AA{\text -} CBR$ and cautious monotonicity questions on a case study on the U.S. Trade Secrets domain, a legal casebase.

In many applications such as recidivism prediction, facility inspection, and benefit assignment, it's important for individuals to know the decision-relevant information for the model's prediction. In addition, the model's predictions should be fairly justified. Essentially, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender. In this work, we focus on the problem of (un)fairness in the justification of the text-based neural models. We tie the explanatory power of the model to fairness in the outcome and propose a fairness-aware summarization mechanism to detect and counteract the bias in such models. Given a potentially biased natural language explanation for a decision, we use a multi-task neural model and an attribution mechanism based on integrated gradients to extract the high-utility and discrimination-free justifications in the form of a summary. The extracted summary is then used for training a model to make decisions for individuals. Results on several real world datasets suggests that our method: (i) assists users to understand what information is used for the model's decision and (ii) enhances the fairness in outcomes while significantly reducing the demographic leakage.