Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowledge for better explainability, however, we were unable to quantify the extent of explainability. In this work, we (1) briefly analyze the definitions of explainability from the perspective of different disciplines (e.g., psychology, social science), properties of explanation, explanation methods, and human-friendly explanations; and (2) propose and formulate an approach to quantify the extent of explainability. Our experimental result suggests a reasonable and model-agnostic way to quantify explainability

Mercer Bell is a customer experience agency. Technically, we were the first in this market as far as being a trademark CX agency. What does that mean nowadays? Nowadays it's a really big complicated broad church of things we do for our clients, including working with aspects of AI. That is everything from deploying it for our clients on an ongoing basis, helping clients message features of artificial intelligence to their clients, and then actually building bespoke things, particularly in the machine learning space for our clients on an ongoing basis.

Covenant Eyes is not the only tech firm to play on these concerns, however. California-based X3watch, for example, offers a similar tracking and reporting feature, albeit one that works by creating a categorised list of the sites users visit that is then shared with their accountability partners. 'This is an opportunity to know and be known,' the X3watch website argues. 'Whether your chosen partner is a friend or a spouse, or you've come across explicit activity on your children's devices, the true goal is liberation that blossoms from open and honest relationships with others who are dedicated to your well-being.' An annual subscription to X3watch is currently priced at $70 (£54) per year.

One of the biggest sources of anxiety about AI is not that it will turn against us, but that we simply cannot understand how it works. The solution to rogue systems that discriminate against women in credit applications or that make racist recommendations in criminal sentencing, or that reduce the number of black patients identified as needing extra medical care, might seem to be "explainable AI." But sometimes, what's just as important as knowing "why" an algorithm made a decision, is being able to ask "what" it was being optimized for in the first place? Machine-learning algorithms are often called a black box because they resemble a closed system that takes an input and produces an output, without any explanation as to why. Knowing "why" is important for many industries, particularly those with fiduciary obligations like consumer finance, or in healthcare and education, where vulnerable lives are involved, or in military or government applications, where you need to be able to justify your decisions to the electorate.

Environmental conflicts have emerged as major issues that deeply affect the socio-economic state of a region and/or an entire nation. These conflicts are related to natural resources, land, wildlife, supply chains etc. The crises are widespread around the globe and it is increasing rapidly. According to the Environmental Justice Atlas, India has the most number of environmental conflicts, followed by Colombia and Nigeria. For instance, approximately 66% of all civil cases in the Supreme court of India are related to the land disputes for more than 2.5 million hectares of land.

"AI will be as central to the white-collar office environment as robotics has been to the production economy," said Mark Muro, senior fellow and policy director of the Metropolitan Policy Program at the Brookings Institution. "They'll fundamentally change what work is and what humans do. And no one gets a free pass." What's happening: A new analysis released Wednesday by Brookings overlaid the keywords in AI-related patents with job descriptions to get a more detailed understanding of which jobs are most likely to be affected by AI -- and where they are. The big picture: Much of the research assessing the workforce impact of these new technologies -- robotics, AI, and machine learning -- lumps them all together under the bucket of automation.

DoNotPay, the "robot lawyer" service that helps you contest parking tickets and even sue people, is launching a new tool to help customers understand license agreements. Called "Do Not Sign," the service is included with DoNotPay's monthly $3 subscription fee, and it lets users upload, scan, or copy and paste the URLs of any license agreements they'd like to check. The service uses machine learning to highlight clauses it thinks users need to know about, including options to opt out from data collection. Agreeing to lengthy license agreements is an almost weekly occurrence for many people, with modern smart devices forcing you to hit "agree" on every new contract. Do Not Sign isn't a replacement for a real lawyer, but it's better than accepting a license agreement sight unseen so you can start using a shiny new gadget, service, or app without delay.

On the first episode of the final season of HBO comedy series Silicon Valley, tech startup engineer Bertram Gilfoyle lets an AI version of himself take over his instant messaging duties. "Do you need the real me for this conversation?" he asks his colleague. It may sound extreme, but the existence of spellcheckers predates the personal computer by a decade. Since 1992, grammar checking has also come as standard in word processors. For the better part of a generation, we've been OK with robots watching and correcting our language, occasional run-ins with Clippy aside.

We can already see the potential for artificial intelligence (AI) in international development: the seemingly endless possibilities to enhance productivity and innovation across healthcare, agriculture, education, transportation, and governance. Yet it is also becoming abundantly clear that AI could have negative repercussions as well, particularly in countries with weaker institutional capacity and legal protections. AI has the potential to threaten democratic processes, employment, human rights and -- because of the weaponization of AI tools -- privacy, policing, and defense. Apart from these potential benefits and threats, the transformative potential of AI for both good and harm will be magnified in the Global South, where existing gender and socio-economic inequalities could either be tempered or exacerbated. Given the opportunities and potential consequences of new automation and mechanization techniques and advanced analysis through machine learning and neural networks, IDRC is investing in applied research across a number of domains to advance the public good with the use of artificial intelligence for development (AI4D).

Artificial Intelligence (AI): The fundamental change in our daily work routine, the way we live and interact with others, is all going to be represented by the fourth industrial revolution. Moving from the third to the fourth industrial revolution is going to open a new chapter in human development – incorporating the extraordinary technological advances. These advanced technologies are emerging and will continue to merge in the business world. We see the fourth industrial revolution changing the digital, physical, and biological worlds. It is creating novel opportunities and promises of a better future. On the other hand, the evolution of technology will become the reason for potential risks and dangers.