In the rapidly evolving landscape of AI technology, addressing ethical concerns and privacy issues is paramount. We’ve gathered insights from eight industry professionals, including Founders and CEOs, to share their strategies. From conducting ethical risk profiling to ensuring human oversight in AI transcription, explore their valuable perspectives on navigating the complex interplay of ethics and AI-driven solutions.
- Conduct Ethical Risk Profiling
- Ensure Transparency in Data Usage
- Implement Fairness Checks
- Source Diverse Data Responsibly
- Clarify Data Practices with Transparency
- Develop Comprehensive Ethical AI Framework
- Prioritize Anonymization and User Consent
- Human Oversight in AI Transcription
Conduct Ethical Risk Profiling
The first step towards ensuring ethical AI is to perform an ethical risk profiling of the application being developed. The risk assessment should ideally identify high-risk or prohibited AI applications.
Development of prohibited applications should be abandoned right at the conceptualization stage, and high-risk applications should be subject first to an internal conformity assessment and later to external assessment by certified assessors and certification authorities.
The city of Vienna developed an application to classify citizen complaints in order to direct them to the appropriate departments. Since such an application is likely to have an impact on the fundamental rights of a citizen in getting timely help from the government body, the city decided to get the application assessed by IEEE on the basis of their CertifAIEd framework.
Certified assessors perform the risk assessment and do an evaluation of the ethical implications based on IEEE ontology specifications for Transparency, Accountability, Algorithmic Bias, and Privacy. The assessor identifies the controls that are relevant and gathers evidence to support compliance of the application with these controls.
Once the “case for ethics” document has been prepared, it is then submitted to a certifying body like TÜV SÜD for gaining the IEEE CertifAIed mark and also entered into the official register of certified applications.
As AI applications become mainstream and start to have an impact on society, governments all over the world are starting to formulate legislation to regulate the potential application of such technology. And in this light, it’s imperative for enterprises to conduct an audit of their AI-driven solution not only from a data privacy and security standpoint but also from the perspective of upholding ethics.
Biju Krishnan
Founder, AI Ethics Assessor
Ensure Transparency in Data Usage
AI is consuming every piece of data possible, and it’s only a matter of time before machine learning violates people’s privacy in some unforeseen way. Companies using AI must be completely transparent in how they are using it, and how the data that they own interacts with it.
At some point, a company will uphold their privacy policy, but the AI that they use will not. It will be interesting to see if there is any accountability in an AI privacy violation. In the meantime, make certain that your personal information is not being fed into AI so that you’re not a part of this when it comes.
Bill Mann
Privacy Expert at Cyber Insider, Cyber Insider
Implement Fairness Checks
Building trust with AI is all about transparency! We use fairness checks to identify and mitigate bias in our training data, ensuring our algorithms don’t inherit any unwanted quirks. For instance, imagine an AI for filtering loan applications.
We’d check for biases based on ZIP code to avoid unfairly penalizing residents of certain areas. This helps us deliver fair and responsible AI solutions!
Aleksey Pshenichniy
Chief R&D Officer, Elai.io
Source Diverse Data Responsibly
When implementing AI-driven solutions, it’s crucial to critically consider the source and distribution of the data used to train the models. From a technical perspective, diverse and representative data improves the accuracy and generalizability of AI systems, enabling them to perform well in real-world scenarios.
Sourcing diverse data helps mitigate potential risks, but it must be done with diligence regarding data privacy and security. By proactively addressing these data and ethics issues, we can work towards building AI systems that are not only technically sophisticated but also inclusive, trustworthy, and socially beneficial.
Ryan Ofman
Head of Science Communication, DeepMedia AI
Clarify Data Practices with Transparency
As a web designer, ethical considerations and user privacy are paramount when implementing AI solutions. Transparency is essential. I explain to clients how data is used to train AI algorithms and its potential privacy impact.
For example, with a content recommendation system, I’d clarify anonymized data practices while outlining how browsing habits inform suggestions. This fosters trust and empowers users.
Juan Carlos Munoz
Co-Founder, CC Creative Design
Develop Comprehensive Ethical AI Framework
In my role as the founder of a software house, addressing ethical considerations and privacy implications in AI implementations is critical, especially as we handle diverse and sensitive client data. To ensure we maintain the highest standards, we’ve developed a comprehensive ethical AI framework that is integral to our operations.
For example, when developing a new AI-driven analytics tool for a retail client, our first step is to rigorously apply principles of data anonymization. This means stripping any personally identifiable information from the data sets used for training our algorithms, ensuring privacy and compliance with data protection laws such as the GDPR. Furthermore, we employ differential privacy techniques, which involve adding noise to the data, making it difficult to trace back any information to an individual.
We also focus on transparency by keeping detailed logs of the AI’s decision-making processes. This is crucial not only for internal reviews but also for client audits, providing both our team and our clients with the ability to review how decisions were made by the AI system. For instance, if our AI tool recommends a specific marketing strategy, both our team and the client can trace back and understand the variables that influenced this decision.
By integrating these ethical practices from the ground up, we not only safeguard the privacy and rights of the individuals whose data we handle but also build trust with our clients, ensuring that our AI solutions are both effective and ethically sound.
Shehar Yar
CEO, Software House
Prioritize Anonymization and User Consent
At Fat Agent, we’ve implemented AI-driven solutions to enhance the user experience and improve efficiency for insurance agents. We prioritize transparency and user consent when addressing ethical considerations and potential privacy implications related to data usage and AI algorithms.
One insight we’ve implemented is ensuring that our AI algorithms are trained on anonymized and aggregated data whenever possible. We mitigate the risk of exposing personal data and uphold user privacy by anonymizing sensitive information. Additionally, we explain how AI algorithms are used within our platform and allow users to opt out of AI-driven features if they have privacy concerns.
For example, when implementing AI-powered chatbots to assist agents with customer inquiries, we ensured that the chatbot interactions were based on general trends and patterns rather than individual customer data. This approach maintains privacy while still providing valuable assistance to users. By prioritizing transparency, user consent, and data anonymization, we strive to implement AI-driven solutions while ethically safeguarding user privacy.
Brad Cummins
Founder, Fat Agent
Human Oversight in AI Transcription
At Ditto Transcripts, we take a proactive stance in addressing the ethical implications surrounding AI and data privacy. Our core philosophy? Prioritize transparency and humanity over efficiency at all costs.
One key example is our use of AI for transcription. While the models vastly increase our speed and accuracy, we have instituted robust human checks. All output is reviewed by our team to ensure nothing was lost in translation and that personal details stay private.
We also have a cross-functional AI ethics board that vets potential use cases through the lens of fairness, accountability, and social impact. If an application raises red flags around bias or privacy invasion, we won’t proceed until we can mitigate those risks responsibly.
Ultimately, we see AI as a supporting tool that should always have human oversight and align with our moral standards. A blind pursuit of optimization is a non-starter if it comes at the expense of upholding ethical principles. Responsible AI adoption is a must for maintaining public trust.
Ben Walker
Founder and CEO, Ditto Transcripts
