"Ethics is knowing the difference between what you have a right to do and what is right to do." Expressed with such clarity by former U.S. Supreme Court Justice Potter Stewart, this statement is especially relevant today as we consider the power of artificial intelligence (AI) and its potential impact on people's lives. As a company intensely focused on developing new AI-fueled applications, at Genesys we understand this technology comes with tremendous responsibility as well as tremendous potential. While the full capabilities of AI have not yet been realised, we need to proceed thoughtfully. Businesses are furiously conceiving ways this intelligent technology can be used to solve everyday challenges, make life simpler and create efficiencies in ways previously unimagined. Its potential seems without limits, which is why we must also consider its effects on society.

From massive face-recognition-based surveillance and machine-learning-based decision systems predicting crime recidivism rates, to the move towards automated health diagnostic systems, artificial intelligence (AI) is being used in scenarios that have serious consequences in people's lives. However, this rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial face recognition systems have much higher error rates for dark skinned women while having minimal errors on light skinned men. A 2016 ProPublica investigation uncovered that machine learning based tools that assess crime recidivism rates in the US are biased against African Americans. Other studies show that natural language processing tools trained on newspapers exhibit societal biases (e.g. finishing the analogy "Man is to computer programmer as woman is to X" by homemaker). At the same time, books such as Weapons of Math Destruction and Automated Inequality detail how people in lower socioeconomic classes in the US are subjected to more automated decision making tools than those who are in the upper class. Thus, these tools are most often used on people towards whom they exhibit the most bias. While many technical solutions have been proposed to alleviate bias in machine learning systems, we have to take a holistic and multifaceted approach. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

What happens when injustices are propagated not by individuals or organizations but by a collection of machines? Lately, there's been increased attention on the downsides of artificial intelligence and the harms it may produce in our society, from unequitable access to opportunities to the escalation of polarization in our communities. Not surprisingly, there's been a corresponding rise in discussion around how to regulate AI. Do we need new laws and rules from governmental authorities to police companies and their conduct when designing and deploying AI into the world? Part of the conversation arises from the fact that the public questions -- and rightly so -- the ethical restraints that organizations voluntarily choose to comply with. According to Edelman's 2019 Trust Barometer global survey, only 56% of the general public has overall trust in the business community.

Electricity can be designed to kill (the electric chair) or save lives (a home on the grid in an inhospitable climate). The same is true for artificial intelligence (AI), which is an enabling layer of technology much like electricity. AI systems have already been designed to help or hurt humans. A group at UCSF recently built an algorithm to save lives through improved suicide prevention, while China has deployed facial recognition AI systems to subjugate ethnic minorities and political dissenters. Therefore, it's impossible to assign valence to AI broadly.

This post originally appeared on PhilipLay.com. To read the post from the original source click here. Earlier this month an article in the Financial Times by John Thornhill, the paper's innovation editor, caught my attention. Thornhill was relaying an intriguing set of ideas expressed by the authors of a new book, What To Do When Machines Do Everything? Before discussing the future impact of today's unfolding industrial innovations such as driverless cars, robotic surgery, precision agriculture, or automated beer service (as in the photo above), the three authors – Malcolm Frank, Paul Roehrig, and Ben Pring – make their first key point, citing the example of an early 19th century innovation that enabled an entire industry that generates $620bn. in annual revenues today.

Pooled development investment fund, Strategic Elements, has added another string to its bow with the launch of a new artificial intelligence and robotics company, Stealth Technologies. Stealth is 100% venture-backed by Strategic and seeks to develop proprietary technologies and collaborate with commercial and government partners. Management said that whilst most artificial intelligence companies focus only on software development, Stealth's multi-disciplinary capabilities allow it to custom build automated robots and create artificial intelligence through machine learning and software development. This includes developing proprietary computer vision technologies, a branch of artificial intelligence that uses machine learning to quickly process and analyse visual data from photos or video like the human visual system can do. Machine learning involves the use of algorithms to perform specific tasks without explicit external instruction.

Artificial intelligence is everywhere these days. Robots are coming to food stores, financial services, law firms and just about everywhere else. Enterprises are using AI to automate their IT networks. The results are promising: AI can identify and fix IT issues faster than humans can. If AI can cook for us, then it is downright preposterous to claim that organizations' data management practices are immune from the relentless tentacles of automation.

Complex contracts usually contain clauses which specify who will own the Intellectual Property (IP) which arises from, or goes into, a project. But what happens when a non-human entity creates original work under the contract? While the fully automated Artificial Intelligence (AI) envisioned in the science-fictional world of killer robots is yet to be realised, smaller-scale AI is commonplace in day-to-day life. AI can be simply defined as technology which performs tasks that would normally require the brain of a human to complete, such as autonomous decision-making. AI programs can have the capacity to learn independently and improve their own processes.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Rep. Alexandria Ocasio-Cortez, speaking at an event in January 2019 honoring the legacy of Dr. Luther King, said, "Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions. And if you don't fix the bias, then you are just automating the bias." Though her comments were correct--algorithms can indeed reflect and exhibit human bias--Rep. Ocasio-Cortez's framing of the intersection of algorithms and fairness highlighted an often-ignored issue in progressive politics. The political movement, defined in part by its commitment to social justice, is unsurprisingly critical of the potential for algorithms, particularly AI, to facilitate discrimination, yet seemingly pays little attention to the ways in which algorithms can actually reduce discrimination.