AI technology has raised concerns in the areas of environmental, social, and governance (ESG). Let’s explore the opportunities and challenges of AI in the workplace.
In 2023, ChatGPT was released, creating both excitement and concerns about the potential of artificial intelligence (AI). People had different opinions, with some seeing AI as an opportunity and others as a threat.
However, it’s not just philosophers and lawmakers who need to consider the implications of AI. Companies, big and small, should be aware of the ESG risks associated with this powerful technology.
Here, we’ll discuss the ethical concerns of using AI in the corporate setting and provide best practices for companies to leverage AI without facing any issues.
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The Ethical Concerns of Generative AI
Social Implications
Many companies are using AI-based tools to simplify their hiring processes. Some are even exploring AI’s potential in making firing decisions. However, the rise of generative AI is expected to disrupt over 12 million jobs by 2030, fundamentally changing how humans are employed. Ed Watal, CEO of Intellibus, a software company, highlights the issue of bias in AI algorithms used for hiring. These algorithms lack transparency, making it difficult to determine the presence of racial, social, gender, or economic bias.
AI systems can inherit biases from historical data, perpetuating existing inequalities and discrimination in hiring and promotion practices. While ESG strategies aim to improve outcomes for specific groups, relying heavily on AI for HR decision-making can hinder these objectives.
On the other hand, well-trained AI models can have positive effects by reducing bias in hiring, focusing solely on skills and qualifications, and promoting inclusivity in the workplace.
Ashu Dubey, CEO of Gleen, a generative AI company, sees the potential for AI to improve fair hiring practices and compliance with labor laws. Generative AI can also be used to train employees, answer questions about interviewing and hiring, and enhance communication of corporate benefits.
Governance Challenges
Governments are increasingly paying attention to AI technology, leading to new policies and regulations that pose compliance risks for companies. Data protection and privacy regulations, such as GDPR in Europe and CCPA in the United States, already require corporations to comply.
The data collected for AI applications or shared with third-party AI service providers can be misused or mishandled, resulting in privacy violations and legal consequences. Data breaches can lead to unauthorized access, disclosure of sensitive information, and damage to a company’s reputation.
From a governance perspective, different organizations have responded to AI in various ways. Some, like Apple, JP Morgan, Verizon, and Amazon, have banned the use of certain AI tools at work. Others have implemented limits on data uploads. Companies are particularly concerned about employees entering confidential data into AI tools.
OpenAI, the parent company of ChatGPT, has introduced ChatGPT Enterprise, which offers a private version of the tool for corporations, preventing confidential information from being leaked. Some corporations, including McKinsey and Walmart, are even building their own generative AI chatbots.
Environmental Implications
One often overlooked consequence of relying on AI is the significant computing power required. Most data centers today are powered by fossil fuels and consume large amounts of water for cooling. Watal expresses concern about the environmental impact of generative AI, as it is expected to contribute to a significant portion of data center energy consumption by 2030.
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Best Practices for AI in 2024 and Beyond
To navigate the early days of generative AI, companies should follow best practice guidelines to maximize opportunities and minimize risks.
Bias and Fairness
AI is only as good as the knowledge and instructions it receives. Ensure ethical guidelines and regulations are followed. Train AI algorithms on diverse datasets and regularly audit them for bias. Implement strategies to mitigate bias and conduct fairness testing.
Data Privacy and Security
Adhere to data minimization and consent principles. Inform individuals about how their data is used in AI systems. Establish clear policies on data retention and disposal to avoid unnecessary data storage. Use encryption measures to protect data from unauthorized access. Regularly audit data practices to ensure compliance with regulations and company policies. Stay updated on cybersecurity best practices.
Human Oversight
While AI can assist employees in decision-making and task completion, it should not replace human judgment entirely. Enforce comprehensive human oversight of AI systems and provide employees with a mechanism to appeal AI-generated decisions.
Managing AI Risk and Opportunity
Rather than embracing or rejecting AI entirely, companies should find a balance between the opportunities and risks associated with AI. Implement initial policies to minimize ESG concerns and ensure a successful future.
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