8 AI Risks Businesses Must Confront And How To Address Them

Zeeshan Ali

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AI News

AI risks are inevitable for companies that use technologies to grow their businesses. They have to deal with them when they apply artificial intelligence. Some of the dangers with AI are similar to those when using any new technology: weak alignment to business goals, a shortage of skills to back up initiatives and a lack of support across the organization.

To overcome these challenges, executives should follow the best practices that have helped the successful adoption of other technologies.

Management consultants and AI experts suggested CIOs and their C-suite colleagues find areas where AI can assist them in achieving organizational goals, design strategies to ensure they have the know-how to support AI programs and establish robust change management policies to ease and hasten enterprise adoption.

However, executives are discovering that AI in the enterprise also brings unique risks that must be recognized and tackled directly.

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8 AI Risks Businesses Must Confront And How To Address Them

8 AI Risks Businesses Must Confront And How To Address Them

AI risks are unavoidable for companies that employ technologies to enhance their businesses. They have to face them when they utilize artificial intelligence. These risk areas can emerge as organizations adopt and apply AI technologies in the enterprise.

1. A lack of employee trust can shut down AI adoption

AI risks are prevalent for workers who are reluctant to accept AI. Professional services firm KPMG, in collaboration with the University of Queensland in Australia, discovered that 61% of respondents to its “Trust in Artificial Intelligence: Global Insights 2023” report are either indifferent or resistant to trusting AI.

According to experts, an AI implementation will be ineffective without that trust. Imagine, for instance, what would happen if workers don’t trust an AI solution on a factory floor that decides a machine must be stopped for maintenance. Even if the AI system is almost always correct, if the user doesn’t trust the engine, then that AI is a waste. Learn About The Unsettling AI Replacing Jobs Statistics You Need to Know for 2024.

2. AI can have unintentional biases

AI works by taking vast amounts of data and then, using algorithms, finds and learns to perform from the patterns it finds in the data. But when the data is skewed or flawed, AI generates erroneous results. Likewise, bad algorithms – such as those that mirror the biases of the programmers – can lead AI systems to produce biased results.

“This is not a theoretical issue,” according to “The Civil Rights Implications of Algorithms,” a March 2023 report from the Connecticut Advisory Committee to the U.S. Commission on Civil Rights. The information illustrated how specific training data could lead to biased results, pointing out as an example that “in New York City, police officers stopped and frisked over five million people over the past decade.

During that time, Black and Latino people were nine times more likely to be stopped than their White counterparts. As a consequence, predictive policing algorithms trained on data from that jurisdiction will overestimate criminality in neighbourhoods with predominantly Black and Latino residents.”

3. Biases, errors greatly magnified by the volume of AI transactions

Human workers naturally have prejudices and blunders, but the impact of their faults is confined to the amount of work they do before the defects are detected – which is usually not much. However, the effects of prejudices or concealed flaws in operational AI systems can be immensely more considerable.

As experts clarified, humans may make scores of mistakes daily, but a bot managing millions of transactions daily amplifies any single mistake by millions.

4. AI might be delusional

AI systems are mostly stochastic or probabilistic. This means machine learning algorithms, deep learning, predictive analytics and other technologies collaborate to analyze data and generate the most likely response in each situation.

That’s different from deterministic AI environments, where an algorithm’s behaviour can be anticipated from the input. But, most real-world AI environments are stochastic or probabilistic and are not flawless. “They give their best estimate to what you’re asking,” said Will Wong, principal research director at Info-Tech Research Group.

In fact, faulty results are frequent enough – especially with more and more people using ChatGPT – that there’s a name for the issue: AI hallucinations. “So, just like you can’t trust everything on the internet, you can’t trust everything you hear from a chatbot; you have to check it,” Wong recommended.

5. AI can create unexplainable results, thereby damaging trust

Explainability, or the capacity to explain and justify how and why an AI system made its choices or forecasts, is another word often used when discussing AI. Although explainability is vital to verify results and build trust in AI overall, it’s not always feasible – especially when dealing with complex AI systems that are constantly learning as they work.

For instance, Wong said AI experts frequently don’t know how AI systems reach those erroneous outcomes labelled as hallucinations. Such situations can hinder the adoption of AI despite the advantages it can offer to many organizations.

In a September 2022 article, “Why businesses need explainable AI and how to deliver it,” global management firm McKinsey & Company stated that “Customers, regulators, and the public at large all need to feel assured that the AI models making significant decisions are doing so in a precise and fair way.

Similarly, even the most advanced AI systems will be ignored if intended users don’t comprehend the rationale for the provided suggestions.”

6. AI can have unintended consequences

AI risks are inherent in using AI, which can have outcomes that enterprise leaders either overlook or cannot foresee, Wong said.

A 2022 report published by the White House, “The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of Americas,” addressed this issue and quoted the findings of Google researchers who examined “how natural-language models understand discussions of disabilities and mental illness and found that various sentiment models discriminated such discussions, creating a bias against even affirmative phrases such as ‘I will fight for people with mental illness.'”

7. AI can behave unethically, illegally

AI risks are involved in some uses of AI that might create ethical conflicts for their users, said Jordan Rae Kelly, senior managing director and head of cybersecurity for the Americas at FTI Consulting. “There is a possible ethical consequence to how you use AI that your internal or external stakeholders might disagree with,” she said.

For example, workers might consider using an AI-based monitoring system both a violation of privacy and corporate excess, Kelly added. Others have expressed similar worries.

The 2022 White House report also emphasized how AI systems can behave in potentially unethical ways, mentioning a case in which “STEM career ads that were clearly intended to be gender neutral were unevenly shown by an algorithm to potential male applicants because the cost of advertising to younger female applicants is higher and the algorithm maximized cost-efficiency.”

8. Employee use of AI can evade or escape enterprise control

AI risks are looming for executives who haven’t prepared for the rise of the technology, according to the April 2023 “KPMG Generative AI Survey”. The survey interviewed 225 executives and revealed that 68% of respondents haven’t assigned a central person or team to coordinate a response to the emergence of the technology, stating that “for the time being, the IT function is leading the effort.”

KPMG also discovered that 60% of those surveyed estimate they’re one to two years away from deploying their first generative AI solution, 72% said generative AI plays a crucial role in building and preserving stakeholder trust, and 45% think it might hurt their organization’s confidence if the proper risk management tools aren’t deployed.

But while executives ponder generative AI solutions and safeguards to implement in future years, many workers already use such tools. A recent survey from Fishbowl, a social network for professionals, found that 43% of the 11,793 respondents used AI tools for work tasks, and almost 70% did so without their boss’s awareness.

Info-Tech Research Group’s Wong said enterprise leaders are creating policies to regulate enterprises’ use of AI tools, including ChatGPT. However, he said companies that banned its use are finding that such limitations aren’t favoured or even possible to enforce. As a result, some are revising their policies to permit using such tools in certain situations and with nonproprietary and nonrestricted data.

How To Manage Risks

AI risks are unavoidable when using AI, but they can be controlled. According to various experts in AI and executive leadership, organizations must first acknowledge and comprehend these risks. From there, they need to establish policies to help reduce the chance of such risks harming their organizations.

Those policies should guarantee high-quality data for training and demand testing and validation to eliminate unintended biases. Policies should also enforce continuous monitoring to prevent biases from infiltrating systems, which learn as they operate, and to detect any unforeseen consequences that emerge through use.

And even though organizational leaders might not be able to predict every ethical issue, experts said enterprises should have frameworks to ensure their AI systems include the policies and limits to produce honest, transparent, fair and unbiased results – with human employees overseeing these systems to verify the results match the organization’s set standards.

Organizations aiming to be successful in such work should engage the board and the C-suite. As Wong said, “This is not just an IT issue, so all executives need to participate in this.”



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