Decisions on where to send police patrol cars, which foster parents to investigate, and who gets released on bail before trial are some of the most important, life-or-death decisions made by our government. And, increasingly, those decisions are being automated. The last eight years have seen an explosion in the capability of artificial intelligence, which is now used for everything from arranging your news feed on Facebook to identifying enemy combatants for the U.S. military. The automated decisions that affect us the most are somewhere in the middle. A.I.'s big feature is essentially pattern matching.

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.

For a variety of reasons and in any number of different industries, video and audio footage are gathered and used for many different reasons. Whether you are working with law enforcement, a lawyer, or are simply using footage gathered on the ground for a news story or an advertisement, you are required to ensure the privacy of individuals and their personal information that may be used to identify a given individual. Not everything captured in a video or audio clip is relevant to what the material is being used for and people have privacy rights that must be adhered to. Every day countless amounts of information are gathered from private individuals without their notice or agreement. It could simply be a smartphone video that someone plans to use for a video blog post, or perhaps it is dashcam evidence in a criminal case. Regardless of what material is gathered and what it is intended to be used for, the onus is on the user to ensure that anyone who is not relevant to a video, case, etc.has their identity protected.

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal rights or regulatory requirements--for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. The technical challenge of explaining AI decisions is sometimes known as the interpretability problem.

How will AI strategy in the enterprise be changed by the widespread attention to systemic racism? Like a lot of complicated topics, the discussion of racism in AI systems tends to be filtered through events that make headline news -- the Microsoft chatbot that Twitter users turned into a racist, the Google algorithm that labeled images of Black people as gorillas, the photo-enhancing algorithm that changed a grainy headshot of former President Barack Obama into a white man's face. Less sensational but even more alarming are the exposés on race-biased algorithms that influence life-altering decisions on who should get loans and medical care or be arrested. Stories like these call attention to serious problems with society's application of artificial intelligence, but to understand racism in AI -- and form a business strategy for dealing with it -- enterprise leaders must get beneath the surface of the news and beyond the algorithm. "I think that racism and bias are rampant in AI and data science from inception," said Desmond Upton Patton, associate professor of sociology at Columbia University. "It starts with how we conceive a problem [for AI to solve]. The people involved in defining the problem approach it from a biased lens. It also reaches down into how we categorize the data, and how the AI tools are created. What is missing is racial inclusivity into who gets to develop AI tools."

In the given unprecedented times, digital transformation is vital. One of the significant challenges is modernizing banks and legacy business systems without disrupting the existing system. However, artificial intelligence (AI) and machine learning (ML) have played a pivotal role in conducting hassle-and risk-free digital transformation. An artificial intelligence and machine learning-led approach to system modernization will enable businesses to associate with other fintech services into embracing modern demands and regulations while ensuring safety and enabling security. In the banking industry, with the growing pressure in managing risk along with increasing governance and regulatory requirements, banks must improve their services towards more unique and better customer service.

Two menacing men stand next to a white van in a field, holding remote controls. They open the van's back doors, and the whining sound of quadcopter drones crescendos. They flip a switch, and the drones swarm out like bats from a cave. In a few seconds, we cut to a college classroom. The students scream in terror, trapped inside, as the drones attack with deadly force. The lesson that the film, Slaughterbots, is trying to impart is clear: tiny killer robots are either here or a small technological advance away. And existing defences are weak or nonexistent.

Mitigate risk management – One of the best examples to showcase the benefits of Machine Learning can be described here. Back in the days, while providing loans to customers, banks had to rely on the client's history to understand the creditworthiness of that respective customer. The process was not always accurate, and banks had to face difficulties in approving the loans at times. But with the digital transformation, the machine learning algorithm analyses the customer in a better way to process the loan in a much convenient manner. Protecting fraudulent activities – Banks are already one of the most highly regulated institutions and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems.

The introduction of artificial intelligence into activities traditionally carried out by human beings produces brutal changes. This is not without consequences for human values. This paper is about designing and implementing models of ethical behaviors in AI-based systems, and more specifically it presents a methodology for designing systems that take ethical aspects into account at an early stage while finding an innovative solution to prevent human values from being affected. Two case studies where AI-based innovations complement economic and social proposals with this methodology are presented: one in the field of culture and operated by a private company, the other in the field of scientific research and supported by a state organization.

Though it was held this year in October instead of September, Apple's "Hi, Speed" media event was a largely typical iPhone launch party, opening with the expansion of its Siri-powered line of HomePod speakers ("Hi"), and concluding with the long-awaited addition of 5G cellular connectivity to the iPhone lineup ("Speed"). Some companies might have tread cautiously on these topics -- Siri and 5G have both been dogged by complaints -- but Apple didn't hold anything back, using a seemingly endless parade of spokespeople to hype the new devices ahead of preorders. The 5G iPhone 12 family, it promised, will "blast past fast," while the $99 HomePod mini will become a hub to "control your smart home," bringing "intelligent assistant" access to the lowest price yet for any Siri device. Having covered Apple for a long time, I'm not surprised that its latest pitches were all sunshine and roses, but I couldn't help but feel that it was making big promises that could come back to bite the company and its partners. As of October 2020, the only thing less likely to thrill someone than a Siri speaker is typical U.S. 5G network performance, which despite boasts of 1-4Gbps downloads has seen average speeds that are barely better than 4G/LTE. Siri and 5G are both theoretically moving targets -- they're services that could improve at any time and in any region without advance notice -- but prior to this event, neither has delivered on its transformative potential.