With the rapid rise of artificial intelligence (AI), many organizations are quickly trying to adapt to and implement new technologies. According to a PwC survey, 73 percent of U.S. companies have already adopted AI in some aspect of their business.
Yet, in the rush to implement AI, some companies ignore the ethical concerns.
“We need to go back and think about that a little bit because it's becoming very fundamental to a whole new generation of leaders across both small and large firms,” says Harvard Business School Professor Marco Iansiti, who co-teaches the online course AI Essentials for Business with HBS Professor Karim Lakhani. “The extent to which, as these firms drive this immense scale, scope, and learning, there are all kinds of really important ethical considerations that need to be part of the management, the leadership philosophy from the get-go.”
You must address these issues to remain competitive in the digital age.
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DOWNLOAD NOWDoes AI in Business Impact the Workforce?
Debates around the ethics of AI in business often focus on its possible negative workforce impacts, particularly in reducing or replacing employees.
In an episode of HBS Online’s Parlor Room podcast, HBS Professor Nien-hê Hsieh, who teaches Leadership, Ethics, and Corporate Accountability, discusses how these concerns aren’t new and why they shouldn’t deter organizations from implementing AI in business operations.
“A lot of the concern around AI is in the context of work,” Hsieh says. “A lot of companies are sort of engaging in using AI to help workers do their work more efficiently, effectively. But at the same time, if you think about the history of technology, there's been lots of technologies that have displaced people from their work.”
Listen to the full Parlor Room episode with Hsieh about ethical AI, or watch it on YouTube.
Consider automated teller machines’ (ATMs) impact on bank tellers. While this shift caused an initial disruption in the banking industry, it also made it easier for banks to open additional branches—spurring demand for tellers—and created new roles that required different skills, such as IT support and customer service.
While AI may lead to the displacement of certain jobs, it also paves the way for new roles. According to the World Economic Forum, around 85 million jobs could be displaced by 2025—with 97 million new jobs emerging that require advanced technical competencies and soft skills, such as leadership, creativity, and emotional intelligence.
“How do you inoculate yourself against the changes that are coming? How do you stay relevant? The answer: develop leadership skills,” writes Patrick Mullane, executive director of HBS Online and Executive Education, in Forbes. “Leading others is something generative AI tools can’t do. The intuition, charisma, relationship-building, and other traits common in a great leader don’t exist in generative AI.”
However, a shifting workforce isn’t the only challenge you must address when implementing AI. Here are five ethical concerns of AI in business that can greatly impact your organization’s success in the digital age.
Ethical Considerations of AI in Business
1. Digital Amplification
Understanding digital amplification is crucial when using AI in your business operations. Digital amplification refers to AI enhancing the reach and influence of digital content.
This can involve algorithms:
- Prioritizing certain information
- Shaping public opinion
- Amplifying specific voices
Imagine a news organization using AI to recommend articles to readers. If the AI system frequently suggests certain news stories over others, those articles receive more attention and clicks, which can greatly shape the public’s opinion.
This phenomenon raises several ethical concerns about fairness, transparency, and the potential for misinformation.
In AI Essentials for Business, Iansiti uses online encyclopedia Wikipedia as an example of how communities can counteract digital amplification. Since the website allows users to post and edit content, opposing sides of an argument can edit one another’s work, getting closer to the truth.
“It's a wonderful testament to the power of communities in policing themselves and becoming better over time,” Iansiti says. “Even the individual editors get better. They have less bias measurably over time as they react to more and more of the community that essentially corrects what it’s saying.”
In the workplace, you can mimic this by encouraging diverse participation in data collection and decision-making, with more open dialogue and regular reviews of AI systems to ensure fairness.
2. Algorithmic Bias
Algorithms are the backbone of AI’s ability to streamline and optimize business operations. Yet, they open your organization to possible bias that can negatively impact you and your employees.
Algorithmic bias is the systematic discrimination that can occur when AI decision-making is influenced by prejudiced data, resulting in unfair outcomes like:
- Discriminatory hiring
- Unequal access to resources
- Workplace bias
Imagine if your company used AI to quickly review applicants’ resumes to help identify the most qualified candidates based on specific criteria. It could streamline the recruitment process, allowing your team to focus on interviewing and evaluating the best matches for the role.
However, if the AI system is trained on biased data—such as the notion that men dominate the finance industry or nurses are primarily female—it may unfairly prioritize candidates and overlook qualified ones from diverse backgrounds.
“We need to be sure that in a world that's driven by algorithms, the algorithms are actually doing the right things,” Iansiti says in AI Essentials for Business. “They're doing the legal things. And they're doing the ethical things.”
To address algorithmic bias, you must ensure your AI systems are built on diverse data sets. You can start by regularly auditing and testing these systems for biased outcomes. You can also encourage a culture of inclusivity by involving a diverse team in the development and review processes.
By taking these steps, you can promote fairness and transparency in your organization's AI applications.
3. Cybersecurity
Cybersecurity is a major ethical concern for AI-driven firms because these systems often handle sensitive data, making them desirable targets for cyberattacks.
Cybersecurity challenges include:
- Phishing: An online scam where cybercriminals disguise themselves as a legitimate individual or organization to trick you into revealing personal information or financial data
- Malware: A combination of "malicious" and "software," it's a program used to gain unauthorized access to companies' IT systems to steal data, disrupt services, or take control
- Ransomware: A type of malware, ransomware prohibits individuals from accessing their data by encrypting their files and demanding a ransom payment to restore them
Several businesses are experiencing an increase in these attacks, with 85 percent of cybersecurity leaders citing that recent attacks are the result of bad actors using AI.
Robust cybersecurity measures tailored to AI are essential for protecting data from unauthorized access, misuse, or breaches. Failing to secure data can lead to financial losses and damage to your organization's reputation.
Your industry often influences how you address cybersecurity. For example, healthcare organizations must comply with strict regulations like HIPAA to protect patient data, requiring robust AI data security measures. In contrast, retail companies need to prioritize protecting customer payment information.
However, there are a few tactics you can use to help with cybersecurity in the age of AI, no matter what industry you’re in.
For example, regularly updating your software and enabling multi-factor authentication are simple yet effective ways to protect your data. In addition, training employees to recognize phishing attempts can significantly reduce the risk of breaches. According to a KnowBe4 report, 86 percent of organizations reduced the threat of phishing attacks after one year of security awareness training.
Simply paring down waste can also help boost your organization’s cybersecurity.
“One thing to be said about all these cybersecurity challenges is that you need to be careful about not keeping data you don't need,” Iansiti says in AI Essentials for Business. “There are so many times that I've heard from an executive, something like ‘I have great data. I have so much customer data. I'm not quite sure what I'm doing with it yet, but I'm keeping it just in case I figure it out.’”
Therefore, you can help avoid catastrophic data breaches by minimizing data storage so protecting it isn’t a huge feat.
4. Privacy
Ethical concerns around AI privacy focus on collecting, storing, and using employee data. AI systems can analyze vast amounts of personal and professional information, which must be properly protected to avoid privacy violations, unauthorized access, and misuse.
“We've got a big privacy problem as our economy becomes increasingly digital,” Iansiti says in AI Essentials for Business. “And interestingly, in some ways, the privacy and the cybersecurity problems are becoming increasingly tied together because one of the big challenges with data isn’t necessarily what the company will do on purpose, but what some rogue agents might do as they get in on the company's networks from the outside illegally and start pilfering all kinds of personal data that they might use in all kinds of nefarious ways.”
To address these concerns, you need to establish transparent data tactics, including:
- Communicating data usage policies to employees
- Implementing strong cybersecurity measures
- Regularly reviewing and updating data practices
With the support of your team, you can build trust throughout your organization that data is secure and private.
5. Inclusiveness
Inclusion is vital in business. Whether it's integrating various groups in decision-making or accepting diverse opinions and ideas in the workplace, inclusion aims to maintain balance.
Yet, with the increasing use of AI, a new kind of inclusion must be addressed.
According to Iansiti, several industries can’t easily leverage AI, and they’re falling behind in today’s economy.
“The divide is fundamentally the digital divide,” Iansiti says in AI Essentials for Business. “That means that anything that's repeated can increasingly be done by an algorithm or a machine. And so those kinds of jobs have stalled, which is one big reason why, for example, manufacturing jobs have been growing more slowly across many economies than non-manufacturing jobs.”
Manufacturing isn’t the only industry feeling left out in the digital economy. Brick-and-mortar retail will likely struggle as well. Now that online retailers leverage AI for personalized marketing, efficient inventory management, and enhanced customer experiences, retail stores quickly lose the “personal touch” that has kept them alive since the online shopping boom.
To address this ethical concern, try fostering a diverse and inclusive environment in both human interactions and technology deployment. Prioritize investing in training and resources for roles that could be left behind due to AI.
By proactively addressing these gaps, you can help create a more balanced and equitable workplace, ensuring that all sectors and individuals have the opportunity to thrive in the digital economy.
Become an Ethical Leader in AI
Creating a workplace of accountability can be challenging in the age of AI, particularly if you aren’t comfortable using these systems yourself. Taking an online course focused on leading in the digital age can make a huge difference.
HBS Online’s AI Essentials for Business course can help you learn how to ethically compete in the age of AI. Throughout the course, you’ll be introduced to industry experts at the forefront of AI who can help you lead your organization through a successful digital transformation.
Want to learn more about the ethics around AI? Explore our online course AI Essentials for Business—one of our online digital transformation courses—and download our online learning success guide to prepare for the program experience.
