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The Difference Between AI Ethics in Theory and in Practice

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As we move forward in the age of artificial intelligence, the ethics of AI become crucial to product design. What seems ethical when developing AI products – programming machines with the ability to choose right from wrong – often changes when putting those products into practice.
 
With body cameras worn by police officers, AI technology allows for facial recognition that could assist in suspect identification. After all, memory can become foggy over time, while what a camera sees may be hard evidence. But what happens when facial recognition leads to bias and prejudice? 

While humans must program machines with ethics in mind, machines lack human reasoning–important when deciding what happens when things don’t go exactly as planned. When AI technology fails, as in the case of the autonomous vehicle that struck and killed a pedestrian, an innocent person is the victim.

David Danks, Carnegie Mellon University philosophy and psychology professor, says the people developing the technology must take into account both – ethics and the business goal – and realize “it is not a zero sum gain. It’s not ethical or profitable, where those are mutually exclusive. It’s not ethical or fast, where those are mutually exclusive.”

Many companies will consider ethical implications of AI when designing products, but only occasionally create dedicated ethics groups to focus on questionable uses of the technology by humans when it’s in practice. Of course, technological flaws and failures must also be taken into account.

Basic AI Design Considerations

Many companies will consider ethical implications of AI when designing products, but only occasionally create dedicated ethics groups to focus on questionable uses of the technology by humans when it’s in practice. Of course, technological flaws and failures must also be taken into account.

Controls must be built in to identify biases, show attribution, and enable course correction as needed. To that end, Constellation Research, a technology research and advisory firm based in Silicon Valley, suggests instilling these five design pillars for AI ethics in all projects:

  1. Transparent: Algorithms, attributes, and correlations should be open to inspection for all participants.
  2. Explainable: Humans should be able to understand how AI systems come to their contextual decisions.
  3. Reversible: Organizations must be able to reverse the learnings and adjust as needed.
  4. Trainable: AI systems must have the ability to learn from humans and other systems.
  5. Human-led: All decisions should begin and end with human decision points.

Learn More About Ethics in Design

Mark your calendar and register today for IEEE’s free webinar on Artificial Intelligence and Ethics in Design, taking place at 1:00 p.m. EST, May 9, 2018. You’ll learn how to help your organization apply the theory of ethics to the design and business of AI systems.

The webinar is the perfect companion to Artificial Intelligence and Ethics in Design, a five-course training created to educate and empower professionals to practically implement ethical considerations when developing intelligent and autonomous products and services.

Resources

Bhavsar, Vrajesh, ARM. (25 Jan 2018). The development of AI ethics must keep pace with innovation. VentureBeat.

Cook, John. (9 Feb 2018). The ethics of AI: Robots will rise, but will they rule us all? GeekWire.

Fingas, Jon. (26 April 2018). Axon opens ethics board to guide its use of AI in body cameras. Engadget UK.

Wang, R “Ray.” (26 Mar 2018). Designing Five Pillars for Level 1 Artificial Intelligence Ethics. Enterprise Irregulars.

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Artificial Intelligence: Friend or Foe of the Healthcare Industry?

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Sometime in the future, Artificial Intelligence (AI) will disrupt healthcare as we know it, but not in the ways most people think. Many fear machines will replace or even turn on humans. But the speed with which computer intelligence is advancing offers far more opportunities than dangers.

AI Variants

Today, AI is shorthand for any task a computer can perform just as well as, if not better than, humans. But there are different forms of AI to consider:

  • Most computer-generated solutions now emerging in healthcare rely on human-created algorithms for analyzing data and recommending treatments, not on independent computer intelligence.
  • Machine learning relies on neural networks (a computer system modeled on the human brain), to simulate and even expand on the way the human mind processes data. As a result, not even the programmers can be sure how their computer programs will derive solutions.
  • In deep learning, which is becoming increasingly useful in healthcare, software learns to recognize patterns in distinct layers. Because each neural-network layer operates both independently and in concert – separating aspects such as color, size and shape before integrating the outcomes – these newer visual tools hold the promise of transforming diagnostic medicine and can even search for cancer at the individual cell level.

Is it All Hype?

AI has been around since 1956, but has made precious few contributions to medical practice. Only recently has the hype begun to merge with reality.

AI hype includes a host of sophisticated new solutions from nurse-bots to AInsurance (insurance powered by AI), to AI wearables for the elderly, to name a few. In general, they’re algorithmic and not true machine-learning approaches. Nearly all have failed to move the needle on quality outcomes or life expectancy.

However, if computer speeds double another five times over the next 10 years, machine-learning tools and inexpensive diagnostic software could soon become as essential to physicians as the stethoscope was in the past.

Deep learning could be the very thing that catapults American healthcare into the future, helping to clarify the best care approaches, creating new approaches for diagnosing and treating hundreds of medical problems, and measuring doctor adherence without the faulty biases of the human mind.

The Hard Truth

Just as Uber and Lyft impacted the taxi industry and robotics the manufacturing industry, technology will have an impact on healthcare.

If technology is going to improve quality and lower costs in healthcare, some healthcare jobs will disappear. According to one study, AI is set to take over 47% of the US employment market within 20 years. Though blue-collar jobs have been in technology’s cross hairs for some time, doctors and other healthcare professionals are starting to feel the pressure, as well.

Over time, patients will be able to use a variety of AI tools to care for themselves, just as they manage so many other aspects of their lives today. But, fortunately for doctors, computers have yet to demonstrate the kind of empathy and compassion that millions of patients rely on in their medical care.

To learn more about AI and how aligning technology with ethical values can help advance innovation, explore IEEE’s new Artificial Intelligence and Ethics in Design Part I course program, available for pre-order now. Upon successful completion, engineers receive valuable CEUs/PDHs that can be used to maintain their licenses. Pre-order for your company now.

Resources

Pearl, Robert. (13 Mar 2018). Artificial Intelligence In Healthcare: Separating Reality From Hype. Barron’s.

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Is Your Industry Ripe for Artificial Intelligence Disruption?

Health industry using AI artificial intelligence healthcare

Artificial intelligence (AI) is poised to make an indelible mark on how business is done in a variety of fields. The AI industry will have tremendous impact on the way people and programs work together effectively. Here are the industries that are primed to see the most tremendous improvements in efficiency and cost savings this year.

Healthcare

AI is now contributing to the practicality of disease management and empowering people to make better lifestyle choices. It’s also creating a better pathway between patients and their healthcare providers. Plus, in a world that’s increasingly vulnerable to cyber security threats, AI can play a vital role in safeguarding the industry overall. It’s not an overstatement to say that the AI industry is crucial for healthcare.

For medical practitioners and providers, AI has led to a breakthrough in emerging drug discovery platforms. Additionally, many treatment centers are using AI-based diagnostics as a first-tier of clinical diagnostics, leveraging AI for enabling personalized medicine.

According to Mark Michalski, executive director of Massachusetts General Hospital and Brigham and Women’s Center for Clinical Data Science, by the end of 2018, half of the leading healthcare systems could adopt some form of AI within their diagnostic groups. It’s anticipated that 2018 will be “the year AI becomes real for medicine.” (Michalski, Dec 2017).

Automotive artificial intelligence AI industry cars AI car industry auto industryAutomotive

There seems to be a new headline about self-driving vehicles permeating every news cycle as of late. Not only is AI “driving” driver-less cars, it can help build smart cars less expensively, ones that are capable of detecting and self-navigating various people, places, and things while in operation. The AI industry and the autonomous vehicles industry really go hand-in-hand.

The most prevalent of AI applications in the auto world is machine learning, which does much of what it sounds. Machines use data to teach themselves how to make real-time decisions in split seconds, similar to how humans learn and improve over time. Thus far, machine learning has been highly useful in developing advanced driving assistance systems. Outside of the vehicles themselves, machine learning has brought huge improvements to the sales and after service functions of the automobile life cycle.

Real Estate

The real estate industry hasn’t had a major breakthrough in decades, making it ripe for disruption. As Value Walk points out, AI has the ability to “reduce operational costs, improve customer service, improve efficiency, and reduce resource wastage” within real estate. AI bots in particular are revolutionizing other industries, and could be one of the first dominant AI technologies that both agencies and property owners adopt. Largely due to their relative ease of integration as compared to other facets of AI, AI bots are able to field queries about leasing, footage, and other common prospective buyer questions that come up during virtual tours.

While the disruption has already begun, Inc. predicts the largest single change that will come to real estate will be AI replacing a realtor for some people.

Bottom Line

The AI industry is expected to expand exponentially in the coming year, completely transforming these and other industries. Programmers, engineers, and technologists need to understand and implement globally accepted ethical considerations at the heart of AI as it continues its fundamental shift in the way business is done. Potential impediments to successfully using this technology, however, includes the practical applications of AI, as well as ethics in design.

This year, IEEE Continuing Professional Education is offering a two part online course program on the subject: Artificial Intelligence and Ethics in Design.  If you’re interested in group discounts for the AI ethics in design courses for your organization, please contact an IEEE Content Specialist today.

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