How to use AI responsibly

Artificial intelligence has rapidly transformed from a science fiction concept to an everyday business tool. 

From chatbots handling customer service to algorithms optimising supply chains, AI is reshaping how we work, communicate, and make decisions. 

While AI can help small businesses, its substantial energy consumption due to computing requirements raises significant environmental concerns.

Still, even in a world where net zero is an imperative due to the impact of climate change, issues around the responsible use of AI are broader than that of energy consumption alone.

What is responsible AI use?

Responsible AI use refers to the ethical, transparent, and accountable deployment of artificial intelligence technologies. 

It takes a comprehensive approach, considering not only AI's capabilities but also its ethical obligations, stakeholder impacts, and long-term consequences.

At its core, responsible AI use involves six key requirements:

1. Transparency: This means being clear about when and how AI is being used, ensuring that users understand they're interacting with automated systems rather than humans. 
2. Fairness: This requires actively working to prevent bias and discrimination, ensuring that AI systems don't perpetuate or amplify existing inequalities. 
3. Privacy protection: This involves safeguarding personal and sensitive data, using it only for intended purposes with robust security measures.
4. Accountability:  Take responsibility for AI systems' decisions and outcomes, even when those systems operate autonomously. This includes having clear processes for addressing problems when they arise and maintaining human oversight of critical decisions.
5. Accuracy and reliability: Understand the limitations of AI systems. Validate their outputs, and avoid over-reliance on automated decisions without human verification. AI is a tool to augment human capabilities, not replace human judgment entirely.
6. Ongoing monitoring and improvement: Regularly audit AI systems for bias, effectiveness, and unintended consequences. When issues are identified you must also be willing to make changes.

Why responsible AI is beneficial

The importance of responsible AI use extends far beyond compliance or public relations—it’s arguably fundamental to sustainable business success, not to mention societal wellbeing.

Risk management

From a risk management perspective, the benefits are:

• Legal protection: Discriminatory algorithms could result in costly lawsuits and regulatory penalties.  Privacy breaches could lead to substantial fines under legislation like the UK‘s data protection legislation. 
• Safeguarding reputation: Biased algorithms or unfair credit scoring systems run the risk of destroying customer trust and damaging brand reputation for years.
• Compliance readiness: The regulatory landscape is rapidly evolving, with governments worldwide implementing stricter AI governance requirements. The European Union's AI Act, for example, establishes comprehensive rules for high-risk AI applications. Organisations that proactively adopt responsible AI practices will be better positioned to comply with current and future regulations.

Competitive advantages

There are also significant competitive advantages to responsible AI use. 

• Recruitment: Businesses known for ethical AI practices could attract better talent, particularly among younger professionals who prioritise working for socially responsible employers. 
• Customer trust: Responsible practices help build stronger brand loyalty and positive word-of-mouth marketing.
• Operational reliability: Ethical AI systems tend to be more robust and sustainable over the long term, as they're built with rigorous testing, diverse data practices, and human oversight that reduces the risk of costly failures.
• Encourages innovation: When teams feel confident that they're using AI ethically, they could be more likely to experiment with new applications and push boundaries in constructive ways. This could lead to competitive advantages and new business opportunities.

By ensuring AI systems are fair, transparent, and beneficial, organisations help build public trust in these technologies, enabling their positive potential to be fully realised.

How to implement a responsible AI action plan

Implementing responsible AI use requires a systematic action plan that touches every aspect of how your organisation deploys these technologies.

Step 1: establish AI governance policies

These should include:

• clear principles for AI use
• defined roles and responsibilities
• processes for reviewing and approving AI implementations
• data usage and privacy guidelines.

These policies should be accessible to all employees, with regular training provided to ensure understanding and compliance.

Step 2: implement robust data management

Since AI systems are only as good as the data they're trained on, ensuring data quality, accuracy, and representativeness is crucial. 

Key practices should include:

• cleaning of datasets regularly
• checking for bias systematically
• ensuring training data reflects the diversity of the populations your AI systems serve
• tracking data lineage to understand where it comes from and how it’s processed.

Step 3: ensure human oversight

It’s a good idea to build in human oversight at every stage. 

While AI can process information and identify patterns faster than humans, human judgment remains essential for context, ethics, and final decision-making. 

Effective Implementation requires that you:

• set clear protocols for the required human review
• ensure people have the ability to override AI recommendations when necessary
• maintain authority structures for final decisions.

Step 4: test and audit regularly

Create systematic processes to:

• test for bias across demographic groups
• validate accuracy continuously
• monitor for unintended consequences
• audit regularly, documenting findings and improvements.

Step 5: prioritise transparency

Transparency should be built into your AI implementations from the ground up. 

For customer-facing applications it’s a good idea to:

• clearly label AI-generated content
• explain to users when they’re interacting with AI systems
• provide clear information about how AI is being used to make decisions that affect them
• consider providing explanations of how AI recommendations or decisions are made.

Step 6: build team capabilities

Ensure your team has the necessary skills and knowledge to use AI responsibly and identify potential issues early. 

This may involve:

• training staff on AI ethics and best practice
• hiring specialists with relevant expertise
• partnering with external experts.

Step 7: establish feedback mechanisms 

These should:

• allow users, employees, and other stakeholders to report concerns or issues with AI systems. 
• create clear processes for investigating and addressing any concerns
• be transparent about any actions taken in response.

ESG considerations in AI consumption

Environmental, Social, and Governance (ESG) considerations are becoming increasingly important in AI deployment, as organisations recognise that responsible AI use extends beyond immediate operational concerns to broader societal and environmental impacts.

Environmental impact

From an environmental perspective, AI systems do have significant carbon footprints. 

Training large AI models requires enormous computational resources, consuming substantial amounts of energy. 

There are actions you could take to mitigate the environmental impact of AI: 

• choose energy-efficient models where possible
• use cloud providers committed to renewable energy
• avoid unnecessary model re-training
• schedule intensive workloads during times when renewable energy is more available on the grid.

Of course, this will depend on the resources at your disposal. 

Social responsibility

The social dimension of ESG in AI consumption focuses on how AI affects communities, workers, and society more broadly. 

This includes asking:

• how does AI affect employment in your organisation?
• are your systems accessible to users with disabilities?
• do they work equally well for all demographic groups?
• are you bridging or widening digital divides?

Responsible AI consumption means actively working to ensure that the benefits of AI are distributed fairly across society.

Governance & accountability

From a governance perspective, responsible use of AI means having clear accountability, being transparent with stakeholders, and ensuring AI use aligns with your business values.

This includes:

• reporting regularly on AI use and impacts
• engaging stakeholders in deployment decisions
• participating in industry initiatives to promote responsible AI development and deployment.
• evaluating the ESG practices of AI vendors. 

Building a Sustainable AI Future

Responsible AI isn't a constraint—it's a strategic advantage that enables innovation, builds trust, and creates sustainable value.

Regardless of the size of your business, you can think about how to develop an approach that adds value.

When it comes to understanding your next steps, you could choose to look at the following:

1. Start with governance policies
2. Focus on data quality
3. Build in oversight from day one
4. Test and iterate continuously.

Organisations that embed responsible AI practices early don't just protect against risks - they may also be positioning themselves to lead in an increasingly AI-dependent world.