As artificial intelligence (AI) becomes more common, so do its risks, such as its potential for bias and privacy infringements. As previously discussed in previous posts, governments around the world are beginning to develop requirements and guidance around AI. Organizations not yet developing AI standards in alliance with these requirements may soon struggle to keep up with regulations. Nevertheless, there are steps they can start taking now to navigate these shifting requirements.

Six Steps for Managing Risk in AI

According to Michael K. Atkinson and Rukiya Mohamed, attorneys at Crowell & Moring specializing in national security practice and regulatory enforcement, AI risk management should be approached like onboarding new employees. Following AI frameworks designed by governmental agencies, the Intelligence Community’s AI Ethics Framework, and the European Commission’s High-Level Expert Group on Artificial Intelligence’s Ethics Guidelines for Trustworthy AI, they recommend six steps to reduce risk in your AI.

1. Build integrity into your organization’s AI from the design stage. “Just as employees need to be aligned with an organization’s values, so too does AI,” Atkinson and Mohamed write in VentureBeat. “Organizations should set the right tone from the top on how they will responsibly develop, deploy, evaluate, and secure AI consistent with their core values and a culture of integrity.”
2. Onboard AI as your organization would new employees and third-party vendors. “As with humans, this due diligence process should be risk-based,” the authors write. This will involve checking the “the equivalent of the AI’s resume and transcript,” such as “the quality, reliability, and validity of data sources used to train the AI.” Additionally, it involves reviewing the risks of using AI whose proprietary data is not available. It also includes checking “the equivalent of references to identify potential biases or safety concerns in the AI’s past performance.” As a further step, organizations should perform “deep background” checks. This includes reviewing source code with the providers’ consent to “root out any security or insider threat concerns.”
3. Ingrain AI into your organizational culture before deployment. “Like other forms of intelligence, AI needs to understand the organization’s code of conduct and applicable legal limits. It then needs to adopt and retain them over time,” Atkinson and Mohamed write. “AI also needs to be taught to report alleged wrongdoing by itself and others. Through AI risk and impact assessments, organizations can assess privacy, civil liberties, and civil rights implications for each new AI system.”
4. Manage, evaluate, and hold AI accountable. Similar to how an organization might take a risk-based, probational approach to responsibilities for new employees, they should do the same with AI. “Like humans, AI needs to be appropriately supervised, disciplined for abuse, rewarded for success, and able and willing to cooperate meaningfully in audits and investigations,” the authors write. They suggest companies routinely and regularly document AI’s performance, including any corrective actions to ensure it produced desired results.
5. Keep AI safe from various dangers, such as physical harm and cyber threats. This is similar to how companies protect employees. “For especially risky or valuable AI systems, safety precautions may include insurance coverage. This is similar to the insurance that companies maintain for key executives,” they write. 
6. Terminate or retire AI systems that don’t meet your organization’s values and standards or that simply age out. “Organizations should define, develop, and implement transfer, termination, and retirement procedures for AI systems,” Atkinson and Mohamed write. “For especially high-consequence AI systems, there should be clear mechanisms to, in effect, escort AI out of the building by disengaging and deactivating it when things go wrong.”

Keeping up with evolving AI requirements and guidelines isn’t easy. However, managing risk in your AI systems isn’t much different from how you are already doing it with employees. Like humans, AI systems are prone to bias and mistakes. As such, it’s fair to treat them with the same level of scrutiny. 

Establishing AI Standards for Your Organization

Artificial intelligence continues to spread across various industries, such as healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments. AI Standards: Roadmap for Ethical and Responsible Digital Environments is a new five-course program from IEEE that provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Atkinson, Michael K. and Mohamed, Rukiya. (19 September 2021). Want to develop a risk-management framework for AI? Treat it like a human. VentureBeat. 

Organizations are increasingly adopting artificial intelligence (AI) standards to mitigate risks associated with the technology, such as its propensity for bias. While developing AI standards is necessary, they also need to be upheld in order to be effective. To do so, organizations can consider establishing a body of experts charged with overseeing AI standards and ethics. 

In general, institutional review boards (IRBs) ensure organizations are upholding their basic ethical principles by authorizing, rejecting, and recommending changes to research projects and products. In the United States, these governing bodies have proven effective at reducing ethical risks in the field of medicine. IRBs can provide similar oversight for organizations involved in artificial intelligence. 

When establishing an IRB for your organization, there are three main issues to consider, according to Harvard Business Review.

Who Should Sit on the Board?

Your IRB should include a diverse group of experts capable of systematically pinpointing and reducing ethical risks in your AI applications. It should include: 

• engineers and product designers who can explain the technology and its potential impact on users;
• lawyers and security officers who are knowledgeable about current laws, regulations, and privacy standards;
• experts who specializes in ethics;
• subject matter experts from various backgrounds who specialize in the application at hand (for example, a doctor’s oversight could be helpful for AI applications used in hospitals);
• and at least one expert who is not affiliated with your organization in order to bring a sense of objectivity to the committee’s decision making.

What Jurisdiction Should the IRB Hold?

When it comes to artificial intelligence applications, try to consult institutional review boards as early as possible, preferably even before research or product development begins. After all, it’s a lot easier and cheaper to make alterations to a project before you start working on it. You wouldn’t want to invest time and money on a project that turns out to be a major ethical risk. 

You also need to determine how much authority your IRB will possess. In the medical field, IRBs are given ultimate authority—once an IRB rejects a proposal, it won’t be reconsidered, and if the IRB proposes changes, the revisions must be made. You’ll need to decide if your IRB has this much power, or if, for example, you want to put an appeals process in place. However, you should keep in mind that the more authority your IRB has, the more effective it is likely to be at reducing risk. 

What are the Values That Will Guide Your IRB?

Developing a core set of values for your IRB will be relatively easy. Rather, the more difficult aspect is instituting mechanisms that prevent these values from being twisted or broadly interpreted.

In the medical field, more than just principles guide decisions. For example, medical IRBs typically compare cases to ones decided upon in the past, which allows IRBs to stay consistent in how they apply principles. 

Similarly, institutional review boards charged with AI oversight can look to previous cases to apply their principles consistently. Let’s say, for example, that your IRB declined to approve a contract with a particular country due to ethical risks related to how that government functions. It could apply the reasoning behind that decision to similar cases in the future. Additionally, if a certain case is unprecedented, an IRB can apply fictionalized scenarios to help it understand how it should apply its principles. 

Setting up an IRB in your organization will help you create a ground-up approach to AI oversight. Additionally, it will build trust among your employees and customers, and make your organization more competitive in an environment where concern over AI is higher than ever. 

Establishing AI Standards for Your Organization

Artificial intelligence continues to spread across various industries, including healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments. AI Standards: Roadmap for Ethical and Responsible Digital Environments, is a new five-course program from IEEE that provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Blackman, Reid. (1 April 2021). If Your Company Uses AI, It Needs an Institutional Review Board. Harvard Business Review. 

When it comes to designing ethical artificial intelligence (AI) systems, developers usually have the best intentions. However, problems often occur when developers fail to follow their intentions, what’s dubbed the “intention-action gap.”

To avoid this, a new report from the World Economic Forum and the Markkula Center for Applied Ethics at Santa Clara University, titled “Responsible Use of Technology: The Microsoft Case Study,” recommends developers follow the lessons listed below.

AI Standards Lessons

1. Before you can innovate responsibly, you must transform your organization’s culture:
   To innovate ethically, you need a company culture that encourages introspection and learning from mistakes. For example, by adopting what Microsoft calls a “hub-and-spoke” cultural model across the various departments that influence product development, Microsoft ensures that security, privacy, and accessibility are embedded into all of its products. This “hub” consists of a trio of internal groups that work like “spokes” within its governance: The AI, Ethics, and Effects in Engineering and Research (AETHER) Committee; The Office of Responsible AI (ORA); and the Responsible AI Strategy in Engineering (RAISE) group. Additionally, Microsoft launched the Responsible AI Standard, a series of steps that internal teams have to follow to support the creation of responsible AI systems.
2. Use tools and methods that make ethics implementation simple:
   With the right technical tools, it will be easier to integrate your new ethics model into the many facets of your organization. Microsoft uses several technical tools—Fairlearn, InterpretML, and Error Analysis—to implement ethics. For example, Fairlearn allows data scientists to analyze and enhance the fairness of machine learning models. Each platform offers dashboards that make it easier for workers to visualize performance. By using checklists, role-playing exercises, and stakeholder engagement, these tools also help teams understand the possible consequences of their products. It also fosters more compassion for how underrepresented stakeholders might be affected.
3. Create employee accountability by measuring impact:
   Make sure your employees are aligned with your company’s ethical values by evaluating their performance against your ethics principles. To do this, Microsoft team members meet with managers for bi-yearly performance evaluations and goal settings to establish personal goals in line with those of the company.
4. Inclusive products are superior products:
   By innovating responsibly through the lifecycle of a product, companies will make products that are better and more inclusive. They can do this by creating principles for AI toolkits that set expectations from the outset of product development.

New Healthcare Industry AI Standard Considers Three Areas of Trust

The Consumer Technology Association (CTA), a working group of 64 organizations, recently created a new standard that identifies the basic requirements for establishing reliable AI solutions. Healthcare organizations involved in the project include AdvaMed, America’s Health Insurance Plans, Ginger, Philips, 98point6, and ResMed.

The standard, released in February 2021 and accredited by the American National Standards Institute, considers three ways to create trustworthy and sustainable AI healthcare solutions:

• Human trust: Consider the way humans interact and how they will interpret the AI solution.
• Technical trust: Address data use, such as data access, privacy, quality, integrity, and issues around bias. Additionally, technical trust considers the technical execution and training of an AI design to provide predictable results.
• Regulatory trust: Ensure compliance to regulatory agencies, federal and state laws, accreditation boards, and global standardization frameworks.

Developing standards for AI applications is difficult, but necessary. By having a plan that integrates ethics throughout your organization, you can better ensure your AI systems are reliable and safe.

Establishing AI Standards for Your Organization

Artificial intelligence continues to spread across various industries, including healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments. AI Standards: Roadmap for Ethical and Responsible Digital Environments, is a new five-course program from IEEE that provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Green, Brian and Lim, Daniel. (25 February 2021). 4 lessons on designing responsible, ethical tech: Microsoft case study. World Economic Forum.

Landi, Heather. (18 February 2021). AHIP, tech companies create new healthcare AI standard as industry aims to provide more guardrails. Fierce Healthcare.

As artificial intelligence (AI) systems are built on larger and larger quantities of data, the potential threat that they can pose to the public also grows. For example, automated systems could be equipped with biased algorithms that discriminate against women and minority groups. Additionally, AI-based software, such as facial recognition technology, can jeopardize the privacy of millions of people. To get ahead of the problem, governments are beginning to regulate rapidly advancing AI. Organizations that develop these systems will eventually be required to comply. 

In Europe, a law known as the General Data Protection Regulation (GDPR) oversees data privacy for EU citizens and residents. While the United States has yet to pass specific AI regulations, the country is expected to begin rolling out state and federal regulations in the coming years. One example is the Algorithmic Accountability Act, which would mandate that organizations examine and repair potentially harmful flaws in computer algorithms. Similar to the General Data Privacy Regulation (GDPR), the Algorithmic Accountability Act would make impact assessments mandatory for automated decision and information systems that are high risk. 

Additionally, many businesses are taking steps to establish their own AI standards. These efforts ensure their systems are ethical and safe, as well as protect them from potential liability. 

Here are four expert-recommended principles organizations can consider for their AI standards.

What Should AI Standards Include?

Transparency:

Huma Abidi, senior director of AI software products at Intel, recommends that AI developers define and create clear, quantifiable standards. These should include processes that can be measured in terms of quality and robustness. She told Venture Beat that ethical AI systems should be “fair, transparent, [and] explainable.”

One example is a paper titled “Datasheet for Datasets.” The paper focuses on a standardized process for machine learning. It documents datasets, which state that “every dataset [should] be accompanied with a datasheet.” This datasheet documents its motivation, composition, collection process, recommended uses, and so on.” Another example is a machine learning documentation project called “Model Cards for Model Reporting.” The paper explains: “Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.”

According to Abibi, these “basic principles” should be built into workflows.

“My point is that like any other software product, you want to make sure it’s robust and all that. However, for AI, you especially—besides having standards and processes—you need to add these additional things,” she told Venture Beat.

A cautious, iterative approach to AI development:

According to Rashida Hodge, VP of North America Go-to-Market, Global Markets, at IBM, businesses should develop cautious, iterative approaches to AI development. The process should include a lifecycle that forces organizations to return to it regularly as the data evolves. They should tailor their AI models to any changes as necessary.

“Just like how we as humans process information and process nuance, as we read more information, as we go visit a different place, we have different perspectives. And we bring nuance to how we make decisions; we should look at AI applications in the exact same way,” Hodge told Venture Beat.

Oversight:

Organizations should avoid siloing their analytics teams. This can inadvertently lead to “analytic city states” that make streamlining technology and ideas challenging. Scott Zoldi, Chief Analytics Officer at FICO recommends organizations appoint a single chief analytic officer. This officer would be responsible for creating and enforcing organizational standards.

“You can safely build more houses when you don’t have to draft a new building code for every house. Likewise, you shouldn’t have to worry about rolling the dice as to which artist will be building your model,” Zoldi wrote in Enterprise AI News.

Professionalization:

It’s important that everyone in an organization is aware of how their job impacts AI development, even if their role is small. As discussed in a previous post, “tactics of professionalization” is one way organizations can standardize AI development broadly across their enterprises. According to these principles, AI developers should set up committed multidisciplinary teams. They should train all their employees and clearly define who within the organization is accountable for the consequences of their AI systems.

Establishing AI Standards for Your Organization

Artificial intelligence continues to spread across various industries, including healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments. AI Standards: Roadmap for Ethical and Responsible Digital Environments, is a new five-course program from IEEE that provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems. 

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Colaner, Seth. (3 January 2020). Evolve: Operationalizing diversity, equity, and inclusion in your AI projects. Venture Beat. 

Brumfield, Cynthia. (8 December 2020). New AI privacy, security regulations likely coming with pending federal, state bills. CSO.

Lucini, Fernando. (24 September 2020). Getting AI results by “going pro.” Accenture Research Report.

Zoldi, Scott. (6 November 2020). It’s Time To Set Industry Standards for AI. Enterprise AI.

Artificial intelligence (AI) is evolving rapidly. According to multinational professional services company, Accenture, businesses spent $306 billion USD on AI applications over the past three years. Despite the advancement of AI, there are currently no specific ethical regulations around the technology—though some governments, including the European Union, are working to establish them. Meanwhile, many organizations are beginning to develop AI standards that will ensure their applications are trustworthy and safe for customers. For example, IBM has taken major steps to build trustworthiness for its AI applications, including the creation of an AI ethics board and AI policies, such as the company’s Principles for Trust and Transparency. 

How Can Organizations Establish AI Standards?

When you receive a meal at a restaurant, you know the food is likely safe to eat. This is because a level of trust exists across the various professionalized fields—the farmers, suppliers, ingredient manufacturers, and restaurant staff who worked to create the meal. However, when it comes to the various stakeholders who are developing AI applications, fewer professionalized roles exist. Furthermore, these roles are not well known among the public. Much like food industry stakeholders, which all must follow specific standards, AI developers should also establish standards that ensure trust.

Successful AI developers establish “tactics of professionalization” across their organizations. According to Fernando Lucini, Global Lead Data Science & ML Engineering — Applied Intelligence at Accenture, developers should set up committed multidisciplinary teams, train their employees, and clearly define who within the organization is accountable for the consequences of their AI systems. To achieves this level of professionalization, he recommends the following steps:

1. Set up definable AI roles within your organization: In professionalized industries like food and agriculture, the roles of teams and individuals responsible for the final product are clearly established and understood. The same rule needs to apply to the role of your AI professionals.
2. Train and educate your AI professionals: Companies need to understand the skills gaps in their AI workforce and provide the necessary supplemental training and education. To keep training consistent, companies should establish career levels for AI professionals and prerequisites. This includes training and coursework designed to help define clear paths for moving up the ranks.
3. Establish formal AI processes: Professionalized industries have a standard way of testing and evaluating products and services. Companies involved in the development of AI need to create similar processes for developing, deploying, and managing AI systems. For instance, they should create clear guidance for employees and teams on how to work with one another. These guidelines should also cover select technologies for the creation of AI and how to then apply those technologies.
4. AI literacy must be democratized across organizations: Organizations need to ensure all departments are educated in AI, even if they do not work directly with the technology. For example, the more your marketing team knows about the AI technology behind an application, the better they will be at communicating its benefits to customers.

Establishing AI Standards for Your Organization

Artificial intelligence continues to spread across various industries, including healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments.

Check out AI Standards: Roadmap for Ethical and Responsible Digital Environments, a new five-course program available on the IEEE Learning Network (ILN) today.

Resources

Rossi, Francesca. (5 November 2020). How IBM Is Working Toward a Fairer AI. Harvard Business Review.

Lucini, Fernando. (24 September 2020). Getting AI results by “going pro.” Accenture Research Report.

While the global pandemic has created an uncertain future for renewables, new discoveries are giving researchers hope for a greener tomorrow. According to a pair of recently published studies from Tel Aviv University, two naturally abundant resources—plants and humidity—may revolutionize renewable energy in the future by generating electricity.

Can Plants Generate Electricity?

One of the studies revealed that plants, which contain chlorophyll, may be able to act as natural solar panels. However, scientists are still determining how the electrical currents of plants can be “plugged into” man-made devices.

“At home, an electric current can be wired to many devices. Just plug the device into a power outlet,” Iftach Yacoby, head of The Laboratory of Renewable Energy Studies at Tel Aviv University’s Faculty of Life Sciences, told CTECH. “But when you want to do it in plants, it’s about the order of nanometers. We have no idea where to plug the plugs. That’s what we did in this study.”

By using a hydrogen-producing enzyme to “sit in the socket” of  the plant cell, the researchers proved that they possess a socket for everything. Even though it was nanotermically-sized, previously it was just a theory. The researchers believe they will now be able to engineer any type of plant or kelp with the purpose of energy production.

Yacoby told CTECH that he wants to use plant enzymes to create ammonia, a compound traditionally used in fertilizers, that doesn’t pollute the environment. “If we can get plants to produce ammonia on their own, we don’t need to produce fertilizer at all. We can give up nitrogen fertilizer and allow plants to use nitrogen in the air without fertilizer,” he said.

While the technology is promising, it won’t be economical for at least another ten years. 

Water Vapor May One Day Charge Batteries

According to another study from Tel Aviv University, water vapor from the atmosphere may one day be harnessed to charge batteries.

Water is able to naturally generate electricity. For example, during thunderstorms, lightning forms along the various stages of cloud formations—beginning with water vapor and then transitioning to droplets and ice. 

In the 1800s, physicist Michael Faraday revealed that metal surfaces can be charged with water droplets. This occurs when there is friction between them.

Knowing that water vapor can create electrical charges during molecular collisions and generate static electricity through friction, the researchers performed an experiment. They sought to identify the voltage between two separate metals when exposed to humidity. They exposed one of the metals to high relative humidity, while keeping the other metal grounded. When the air was dry, there was no charge. When they elevated the humidity to over 60%, however, it did generate a voltage. This voltage then dissipated when they lowered the humidity.

The findings contradict traditional thinking about humidity as it pertains to electricity. While water is considered an effective conductor of electricity, it has not traditionally been seen as a way to produce charges on surfaces. “However, it seems that things are different once the relative humidity exceeds a certain threshold,” Professor Colin Price told Science Daily. 

According to the findings, it may be possible for humid air to charge metal surfaces to roughly a single volt. 

“If a AA battery is 1.5V, there may be a practical application in the future: to develop batteries that can be charged from water vapor in the air,” Price said. “The results may be particularly important as a renewable source of energy in developing countries. In these areas, many communities still do not have access to electricity, but the humidity is constantly about 60%.”

In other words, given the abundance of humidity in warmer climates, the technology could potentially serve as an endless source of renewable energy in poorer regions that need it the most.  

Connecting Distributed Energy Resources

Leveraging distributed energy resources (DERs) and microgrids can help countries reach their renewable energy goals.

Introduction to IEEE Std 1547-2018: Connecting Distributed Energy Resources is a course program that focuses on IEEE Standard 1547-2018. This standard provides technical specifications for interconnection and interoperability between utility electric power systems (EPSs) and distributed energy resources. It also provides requirements relevant to the performance, operation, testing, safety considerations, and maintenance of the interconnection.

Contact an IEEE Content Specialist today to learn more about getting access to these courses for your organization.

Do you want to learn more about Standard 1547 for yourself? Visit the IEEE Learning Network.

Resources

American Friends of Tel Aviv University. (9 June 2020). Water vapor in the atmosphere may be prime renewable energy source. Science Daily. 

Kabir, Omer. (8 June 2020). The sun’s rays can electrify plants into producing renewable energy, study finds. CTECH.

You may not even be aware of it, but we use standards every day. In industries from communications and media, to healthcare, construction, energy and more, standards help ensure safety, reliability and environmental care, and contribute to the enhancement of our daily lives.

Standards are published documents that establish specifications and procedures designed to ensure the reliability of the materials, products, methods, and/or services people use every day. In engineering and technology industries, technical standards establish uniform engineering or technical criteria, methods, processes and practices developed through an accredited consensus process.

Using Standards All Day, Every Day

Providing a common global language for product development, standards make it possible for cell phones to communicate with each other anywhere in the world, for bank cards to fit into any cash machine, for consumers to buy a light bulb for just about any lamp in any store, and for them to be able to plug that lamp into an electrical outlet.

Standards are everywhere and play an important role in the economy. They provide:

• Safety and reliability. Users perceive standardized products and services as dependable, which raises user confidence, increases sales and grows technology acceptance.
• Government policy and legislative support. Regulators and legislators refer to standards to protect user and business interests.
• Compatibility and interoperability. When products and services comply with standards, devices work together.
• Business benefits, such as market access, economies of scale and innovation.
• Consumer choice. Standards provide the fundamental building blocks for product development and make it easier to understand and compare competing products. Mass production based on standards provides a greater variety of accessible products to consumers.

To learn more about technical standards and standards development, check out Mars Space Colony: A Game of Standardization. It’s the first standards development simulation game, crafted by experts with 20+ years’ experience in high-stakes, real-world technical standards development.

Resources

(14 October 2015). Imagine a world without standards. Touchstone.

Why we need standards. ETSI.