Domestic AI agents faces ethical, autonomy, and inclusion challenges, particularly for overlooked groups like children, elderly, and Neurodivergent users. We present the Plural Voices Model (PVM), a novel single-agent framework that dynamically negotiates multi-user needs through real-time value alignment, leveraging diverse public datasets on mental health, eldercare, education, and moral reasoning. Using human+synthetic curriculum design with fairness-aware scenarios and ethical enhancements, PVM identifies core values, conflicts, and accessibility requirements to inform inclusive principles. Our privacy-focused prototype features adaptive safety scaffolds, tailored interactions (e.g., step-by-step guidance for Neurodivergent users, simple wording for children), and equitable conflict resolution. In preliminary evaluations, PVM outperforms multi-agent baselines in compliance (76% vs. 70%), fairness (90% vs. 85%), safety-violation rate (0% vs. 7%), and latency. Design innovations, including video guidance, autonomy sliders, family hubs, and adaptive safety dashboards, demonstrate new directions for ethical and inclusive domestic AI, for building user-centered agentic systems in plural domestic contexts. Our Codes and Model are been open sourced, available for reproduction: https://github.com/zade90/Agora

As summer winds down and the familiar hum of school buses returns to our neighborhoods, millions of American students are gearing up for another year of learning. But as we stand on the cusp of an artificial intelligence (AI) revolution, this annual ritual is about to face a seismic shift--especially for students with intellectual and developmental disabilities (IDD). The decisions that school leaders make in the next academic year are likely to determine whether this technological wave creates more inclusive learning environments, or exacerbates existing disparities. A recent study from the Special Olympics Global Center for Inclusion in Education reveals a complex landscape of attitudes towards AI in education and a fear of leaving students with IDD behind. The study found the majority of educators (64%) and parents (77%) of students with IDD view AI as a potentially powerful mechanism to promote more inclusive learning.

The new era of artificial intelligence (AI) has arrived, opening up a world of opportunity for significant progress, but also exposing the growing gender gap in digital literacy. UN Women highlights the major gender biases that have been identified in AI tools. In addition, the majority of tools are designed from a male perspective, further excluding women from usability. Interdisciplinary researchers were given access to the development stages of ChatGPT and its successor, GPT-4, and reported that "the solution may represent various societal biases and worldviews that may not be representative of the user's intent or widely shared values". As an entrepreneur, a woman, a mother, and a woman of color, I am committed to promoting inclusivity and equality in the business world.

Guidance to help your organization procure and implement inclusive artificial intelligence (AI) hiring technologies. Many employees from across your organization can influence how technology factors into the hiring process. This process can include staff who work in hiring, procurement, information technology, leadership, and other roles. Depending on the size of your organization, a single employee might work across multiple domains. Regardless, as your organization considers adopting new hiring technologies, relevant staff members should make sure accessibility and equity are top of mind.

In the past, data teams and other jobs dealing with data, we're still pretty much in the wild west, meaning all of it is new territory & is yet to be explored. Certain best practices have been uncovered in recent times, but for the most part, there's not any one proven method to follow and the fact that the job titles of data professionals' (and the roles they play) differ widely is another evidence of this. One of the forks in the path for the future of how data teams will evolve, roles in data, and even the field of artificial intelligence (AI) in general is how AI ought to be inclusive (that includes the various types of people with different roles, working together towards an end objective) as well as exclusive (siloed to particular and specific teams in order to get the job accomplished more precisely and effectively). Which direction AI veers will be able to alter the core structure of companies and even individual career paths. So, what is the future -- inclusive or exclusive?

Data diversity: Data diversity refers to how wide of a net is cast by your dataset. Does it cover all of your potential use cases, including those that may be less common? Does it represent all of your end users and how they may interact with your model? Lack of coverage is one of the top reasons why companies fail in their endeavours to build more inclusive AI. It's this step that requires some of the most careful consideration in the AI life cycle.

Following the enthusiastic feedback we received on our previous article on Governance & Policy within AI, this time we decided to focus on the important link between AI and International Cooperation. This article aims to throw light on all the current trends on International Cooperation within AI to better understand the recent advancements and tackle the existing challenges. To this end, first, we give a general understanding of the International Cooperation'aspect' within AI. Secondly, we will go through the issues that are not allowing International Cooperation to fully unleash its potential for a beneficial AI. Therefore we have interviewed prominent experts in the field.

Orange launched its strategic plan for the next five years, Engage 2025, last December and AI-enabled innovation was one of the four pillars of the group's future success. This breaks into four parts, as shown below. The 2025 strategy states, "Our ambition is as strong as our social commitments are firm. And we will never think of one without the other" (see last section of article on inclusivity). Lugagne-Delpon said at the online briefing, "We believe that AI can bring value to almost every phase of the network lifecycle – so network planning and design to optimise the efficiency of investment, operations for advanced monitoring, smarter maintenance and better security, and also optimisation to populate a number of operation processes and also optimise the performance and the use of resources." He went on to describe a number of use cases.

The pandemic has resulted in organizations shifting to remote work environment overnight. Artificial intelligence tools are being increasingly adopted by organizations to ease the pressure on employees, boost cost-efficiency, and accelerate the complex hiring process. AI algorithms are considered as tools to eliminate human subjectivity, such as bias from the employees' hiring process. IT leaders have warmed up to the prospect of hiring remote employees during the pandemic. Remote hiring has the advantage of enabling organizations to not stay limited to candidates from a given geographical location, now competent candidates can apply from across the world. Hiring managers state that filtering through the sheer volume of applications can be daunting.

In recent years, a growing number of organizations have utilized artificial intelligence (AI) to revolutionize their traditional workflows. These systems are implemented to enhance cost-efficiency, reduce employee burnout, and even identify premium talent. Many organizations are using AI tools to expedite the arduous hiring processes. These algorithms have been viewed as objective tools capable of eliminating human subjectivity from the employment screening process. Paradoxically, many of these models are riddled with the same inherent biases these systems are intended to remove.