As artificial intelligence reshapes businesses, companies face a balancing act around implementing AI tools to streamline operations and customer experiences while ensuring responsible AI use and addressing the ethical and practical challenges.
Ordinary business owners and the minds behind its creation have widely raised questions of fairness, transparency, and privacy concerns in AI. It even reached the European Parliament in 2020, discussing important questions like, how do we:
- ensure we don’t perpetuate existing biases or create new ones?
- protect individual privacy in an era of rampant cyber attacks?
- maintain human accountability when machines are making increasingly complex decisions?
Ethical AI Adoption
While addressing biases, privacy, and transparency might be part of your business as usual, AI amplifies these aspects, which is why AI ethics should be on your radar.
Types of Biases
- Assumptions based on race, gender, age
- Data that doesn’t represent the full population
- Errors in how data is collected or measured
- The algorithm itself produces biased results
- Misrepresentation of real-world event reporting
- Over-relying on AI suggestions instead of human judgement
- Perpetuating past inequalities through historical data
- Reinforcing pre-existing beliefs or preferences.
The developers’ own biases can also inform your product/system:
- Deciding which data points (features/categories) are relevant
- Designing and structuring algorithms can embed certain assumptions
- How developers analyse and act on AI outputs can affect the interpretation of results.
Responsible AI Use: Privacy and Transparency
Privacy concerns arise as AI systems often require vast amounts of data to function. Robust data protection measures that clearly communicate data usage policies, and comply with regulations like GDPR or CCPA are essential to protect sensitive customer and employee information.
Transparency means being open about when and how you’re using AI. This can be challenging, as some AI algorithms operate as ‘black boxes’, making their decision-making processes difficult to explain. However, transparency builds trust with customers and employees, helps identify and correct biases, and may soon be a regulatory requirement in many jurisdictions.
AI Risk Management: Ensuring Fairness and Accountability
While it can feel complicated and overwhelming, there’s opportunity for growth and learning through the considered planning that’s required to “get it right”.
Start with diverse data, as it helps prevent bias. For example, ensure customer data represents your entire customer base. For publicly available datasets, look for ones that include a wide range of demographics. Use an AI fairness tool – many are open-source and free.
Always include human oversight to catch nuances that AI might miss and provide a layer of accountability:
- Have employees review decisions, especially for important or sensitive matters
- Ensure there’s a process for customers to appeal to an actual human in the business.
Start small and scale so you can learn and adjust with lower stakes. Begin with responsible AI use in less critical business areas and gradually expand as you become more comfortable managing the systems. Lastly, document everything for accountability and to trace and fix issues.
Strategy, Digital, and Everything In Between
We’ve witnessed first-hand the excitement and trepidation surrounding AI adoption among small businesses wanting to stay competitive and outsource repetitive and time-consuming tasks.
Our team is here to support your business to access AI tools as part of our strategic business growth consulting service.
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