Current bias evaluation methods rarely engage with communities impacted by AI systems. Inspired by bug bounties, bias bounties have been proposed as a reward-based method that involves communities in AI bias detection by asking users of AI systems to report biases they encounter when interacting with such systems. In the absence of a state-of-the-art review, this survey aimed to identify and analyse existing AI bias bounty programmes and to present academic literature on bias bounties. Google, Google Scholar, PhilPapers, and IEEE Xplore were searched, and five bias bounty programmes, as well as five research publications, were identified. All bias bounties were organised by U.S.-based organisations as time-limited contests, with public participation in four programmes and prize pools ranging from 7,000 to 24,000 USD. The five research publications included a report on the application of bug bounties to algorithmic harms, an article addressing Twitter's bias bounty, a proposal for bias bounties as an institutional mechanism to increase AI scrutiny, a workshop discussing bias bounties from queer perspectives, and an algorithmic framework for bias bounties. We argue that reducing the technical requirements to enter bounty programmes is important to include those without coding experience. Given the limited adoption of bias bounties, future efforts should explore the transferability of the best practices from bug bounties and examine how such programmes can be designed to be sensitive to underrepresented groups while lowering adoption barriers for organisations.

Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a secure database, and by using a custom dataset, captured some performance metrics which included student evaluation scores, Research Publications, Years of Experience, and Administrative Duties. Multilayer Perceptron (MLP) algorithm was utilized due to its ability to process complex data patterns and generates accurate predictions in a lecturer's performance based on historical data. This research focused on designing multiple performance metrics beyond the standard ones, incorporating student participation, and integrating analytical tools to deliver a comprehensive and holistic evaluation of lecturers' performance and was developed using Object-Oriented Analysis and Design (OOAD) methodology. Lecturers' performance is evaluated by the model, and the evaluation accuracy is about 91% compared with actual performance. Finally, by evaluating the performance of the MLP model, it is concluded that MLP enhanced lecturer performance evaluation by providing accurate predictions, reducing bias, and supporting data-driven decisions, ultimately improving the fairness and efficiency of the evaluation process. The MLP model's performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), achieved a test loss (MSE) of 256.99 and a MAE of 13.76, and reflected a high level of prediction accuracy. The model also demonstrated an estimated accuracy rate of approximately 96%, validated its effectiveness in predicting lecturer performance.

House Republicans argue in a new congressional report that hundreds of millions of dollars in federal research funding over the last decade has contributed to China's military technological advancements. Collaborations between U.S. and Chinese academics have led to research publications related to advanced research on topics like hypersonics, directed energy, nuclear and high energy physics, and artificial intelligence and autonomy. That information, Republicans argue, could be weaponized against the U.S. in the event of war with China. Some of the collaborative research they identified related to military applications like high-performance explosives, tracking of targets and drone operation networks. The House Select Committee on China Competition, together with the Education and Workforce Committee, found some 9,000 joint research publications that were funded either through the Department of Defense (DOD) or the Intelligence Community (IC) published by co-authors with ties to China's "defense and security apparatus," including entities that are on a Commerce Department blacklist.

GPT-Vision has impressed us on a range of vision-language tasks, but it comes with the familiar new challenge: we have little idea of its capabilities and limitations. In our study, we formalize a process that many have instinctively been trying already to develop "grounded intuition" of this new model. Inspired by the recent movement away from benchmarking in favor of example-driven qualitative evaluation, we draw upon grounded theory and thematic analysis in social science and human-computer interaction to establish a rigorous framework for qualitative evaluation in natural language processing. We use our technique to examine alt text generation for scientific figures, finding that GPT-Vision is particularly sensitive to prompting, counterfactual text in images, and relative spatial relationships. Our method and analysis aim to help researchers ramp up their own grounded intuitions of new models while exposing how GPT-Vision can be applied to make information more accessible.

We're excited to share that Gartner has recognized Google as a Leader in the 2023 Gartner Magic Quadrant for Enterprise Conversational AI Platforms, authored by Bern Elliot and Gabriele Rigon. We believe this recognition is a testament to Google Cloud's robust investments and commitment to innovation in AI, coupled with a deep understanding of enterprise customer needs. Enterprises are increasingly investing in AI-driven solutions that balance addressing customer expectations with operational efficiency. At a time when the demand for quality, performant, and trustworthy conversational AI has never been higher, we're thrilled to continue to deliver best-in-class technologies, purpose-built to solve our customers' most critical use cases. In 2022, Google Cloud delivered cutting-edge conversational AI technologies with many launches to our Conversational AI API portfolio.

DeepSee.ai, the creator and leading provider of Knowledge Process Automation (KPA), announced that it has been inducted into the JPMorgan Chase Hall of Innovation. The bank's Hall of Innovation award recognizes select emerging tech companies for their innovation, business value, and disruptive nature. "We're extremely honored to accept this award from JPMorgan Chase" "DeepSee has helped us automate manual post-trade checks supporting complex derivatives trading into AI-powered business outcomes," said Tom Damico, Global Head of Equities Operations, JPMorgan Chase. "We're already seeing efficiencies in post-trade processing and reconciliations, with more efficient deal review timeframes and more importantly, reduced operational risk." "We're extremely honored to accept this award from JPMorgan Chase," said Steve Shillingford, CEO of DeepSee.

Gartner, Cool Vendors in Storage and Hybrid Infrastructure -- Modernize Legacy, Prepare for Tomorrow,Jerry Rozeman, et, 25 May 2021. The Gartner Cool Vendor badge is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact.

What do we talk about when we talk about AI ethics? Just like AI itself, definitions for AI ethics seem to abound. A definition that seems to have garnered some consensus is that AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technologies. If this definition seems ambiguous to you, you aren't alone. There is an array of issues that people tend to associate with the term "AI ethics," ranging from bias in algorithms, to the asymmetrical or unlawful use of AI, environmental impact of AI technology and national and international policies around it.

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

WIRE)--Five9, Inc. (NASDAQ: FIVN), a leading provider of the intelligent cloud contact center, today announced that it has been positioned as a Leader in the first Aragon Research Globe for Conversational AI. The report notes strengths for Five9, including its focus on self-service, seamless handoff between bots and live agents, and App Marketplace for integrations and channels. Aragon Research analysts evaluated 22 conversational AI providers based on three dimensions: strategy, performance, and reach. The report notes that providers positioned as Leaders have comprehensive strategies that align with industry direction and market demand, and effectively perform against those strategies. Five9 is focusing on AI as one of its key innovation strategies.