AI Requires Responsible Use. Businesses Need Innovation. Here’s How to Strike a Balance

AI Ethics: The Need of the Hour

While artificial intelligence (AI) and machine learning (ML) have been around for decades, the public release of ChatGPT in late 2022 ushered in a new era of AI implementation.

71% of respondents in McKinsey’s 2025 State of AI: Global Survey asserted that their company regularly utilized Gen AI in at least one business function – a jump from 65% in 2024. The use of AI in business will only expand, with the market volume projected to reach USD 1.01 trillion by 2031.

However, with this expansion comes ethical concerns about privacy, AI governance, bias, plagiarism, and other issues. While the intention is usually to optimize business processes and improve outcomes, certain AI applications may come with consequences, such as unfair customer treatment that damages company reputation or failure to comply with regulations such as the GDPR.

As such consequences increasingly come to light and AI systems become more autonomous and ubiquitous, new guidelines are emerging to address growing apprehensiveness about AI ethics. Ensuring accountability, transparency, and fairness is more critical than ever.

In this article, we discuss current concerns about AI ethics, as well as balancing ethical AI with innovation.

What is AI Ethics?

AI ethics refers to the principles that are meant to ensure the fair and responsible development and use of AI technologies. It includes a wide range of considerations, including but not limited to accountability, transparency, fairness, privacy, and potential societal and environmental impact.

Furthermore, ethics in AI broadly covers two aspects: the behavior of humans in the development, use, and treatment of AI, and the behavior of the AI systems themselves.

Major Concerns About AI and Ethics

Privacy

While AI systems tend to involve the same privacy risks we have faced for the past decades of Internet usage and data collection, they operate on an unprecedented scale. AI systems lack transparency and require so much data that we have less control than before over what information is collected, how it is used, and how we may delete or correct it.

The most prominent AI privacy risks include collection of data without consent, use of data without permission, unchecked bias and surveillance, data leaks, data exfiltration, and collection of sensitive data.

Environmental Impact

The development and use of AI – especially Gen AI – has a staggering impact on the environment. Data centers, which are used to run and train deep learning models for Gen AI tools such as ChatGPT and DALL-E, require massive amounts of energy.

Noman Bashir, Computing and Climate Impact Fellow at MIT MCSC, says, “The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants.”

To put the energy use of Gen AI into context: according to the International Energy Agency, a single request made through ChatGPT takes 10x the electricity of a Google Search. That’s about 3 watt-hours of electricity.

Accountability

As AI systems become more widespread and autonomous, accountability becomes crucial.

For example, who should bear responsibility when an AI system leads to harm or generates an error: the developers who created the system, the organization or individual that utilized it, or the system itself?

Such questions become even more critical in scenarios where errors can lead to life-threatening consequences, such as healthcare diagnostics.

Discrimination and Bias

Issues with bias in AI systems have existed since before the explosion of Gen AI. For example, in 2014, Amazon realized their newly built AI recruiting tool was biased against women – a reflection of the male-dominated tech world.

Gen AI tools come with similar problems at a large scale, and sometimes generate text and images that reinforce biases related to race, gender, and more.

As AI becomes increasingly ubiquitous, it is crucial that we navigate it with sensitivity, nuance, and intention.

How to Balance Ethical AI and Innovation

Balancing innovation and AI ethics requires a multidisciplinary approach involving collaboration between governments, corporations, and individuals. Here are some key methods to strike that balance.

Address Existing Frameworks

In many cases, it will be more effective for policymakers to update existing frameworks to adapt to AI, rather than to develop new ones. This will, however, require a balancing act between ensuring that the frameworks address AI’s risks and leaving breathing room for innovation.

Many laws that currently govern data privacy and intellectual property were not created with AI – particularly Gen AI – in mind. For instance, Gen AI poses fresh challenges in the realms of intellectual property and copyright. Gen AI models are often trained on huge datasets without the explicit consent of authors and artists, raising questions about fair use and ownership.

Policymakers must identify the gaps in existing frameworks, determine where updates or new regulations are necessary, and make sure that regulatory bodies can enforce them.

Implement Privacy Best Practices

By following AI privacy best practices, organizations can build trust with stakeholders and better comply with data privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the EU Artificial Intelligence (AI) Act, India’s Digital Personal Data Protection Act (DPDPA), and the UAE’s Personal Data Protection Law (PDPL).

Major privacy best practices include restricting data collection, offering additional protection for sensitive data, performing risk assessments, obtaining and verifying consent, and reporting on data collection and storage practices.

Nurture a Gen-AI-Ready Workforce

Many employees already use Gen AI on a regular basis. It is critical that they are knowledgeable about both the technology’s uses and its flaws; crucially, they should understand the impact of the development and use of Gen AI tools. This will help employees leverage Gen AI in a more responsible and efficient way.

Hence, companies should offer training and support for AI ethics and processes. Programs such as OpenAI Academy can help staff understand AI in a comprehensive manner and gain hands-on experience with AI tools.

Furthermore, when developing your AI policies, invite your employees to provide their opinions on AI. Considering your teams’ attitudes towards AI is key to balancing AI ethics with business innovation.

Lastly, leaders should assure their employees that AI systems will be used to optimize their work, not replace them.

Prioritize Sustainability

While governments are developing AI strategies, they often do not take into account AI’s deep environmental impact. The lack of environmental safeguards is potentially AI’s biggest risk.

Here are some initial steps that governments and corporations can take:

• Countries can integrate AI policies into their wider environmental regulations.
• Tech companies can improve the efficiency of AI algorithms to lower energy consumption, while also recycling water and reusing components when possible.
• Governments can implement regulations that require companies to disclose the direct environmental impacts of AI-driven products and services.
• Companies can make their data centers more eco-friendly through methods such as using renewable energy and offsetting their carbon emissions.
• Countries can create standardized methods to assess the environmental impact of AI, as reliable data on this issue is currently lacking.

Create Accountability Frameworks

It is important to create clear accountability frameworks for AI systems. Defining who is responsible ensures that those involved can be held accountable for any potential consequences of using AI.

This includes implementing processes to fix errors and offer recourse if harm occurs. It also means setting standards for building and deploying AI, including thorough testing to reduce risks.

Additionally, there must be caution against using AI to justify otherwise unethical decisions by attributing them to the “neutral” machine, which could lead to shirking human responsibility.

Remain Agile

Both governments and companies must prepare for the fast-changing future of AI.

Because AI is developing so quickly, flexible and forward-thinking governance is needed. Traditional regulations often cannot keep up, and with AI, the consequences of falling behind are more serious.

Looking ahead is crucial, especially as AI merges with other technologies like neurotechnology and quantum computing. For example, AI can also spread disinformation or create realistic deepfakes on a large scale, which could threaten democracy and public trust.

To tackle these issues, policymakers need flexible regulations that can grow with AI. This means assessing risks, building AI expertise within government, and working with other countries to create shared standards.

Conclusion

As AI technology marches on, the distinction between human and machine-based decision-making is becoming increasingly blurred. This phenomenon raises complex ethical questions regarding fairness, bias, autonomy, agency, environmental impact, and privacy.

AI ethics provide a critical framework to navigate these questions when developing, deploying, or using AI. By prioritizing the responsible use of AI, we can unlock the technology’s full potential while ensuring the fair treatment of both people and the environment.

A full commitment to ethical AI will require the collaboration of governments, organizations, and individuals. However, this is a daunting task. That’s why Silverskills is here to help with the first steps for organizations.

We provide AI services that include advisory and proof of concepts, data mining and analytics, process automation, multiple systems integration, and more. Contact us now to leverage responsible artificial intelligence and future-proof your company.