Breaking Barriers - Empowering Women in AI: Navigating Ethical Innovation, Career Paths, Authenticity and Self-Discovery

Starting an Intentional Mentorship Journey in AI Ethics

Imagine a bright morning on March 4, 2024, in bustling cities like Dublin and Bangalore. It marked the start of something special—a journey not across borders, but through the world of AI ethics.

Meet Archana Rao V and me, two individuals from different professional backgrounds, united by the Women in AI Mentorship Program. Archana, with her roots in AI ethics research, had recently ventured into the corporate world. Meanwhile, I brought years of corporate tech experience to the table and sought guidance for navigating the AI landscape.

Our first conversation felt like meeting long-lost friends. We bonded over shared interests in AI ethics, philanthropy, and self-growth.

In our inaugural session, Archana shared insights from her journey, diving into the pillars of ethical AI—transparency, privacy, accountability, and bias. Intrigued by these topics, I found myself drawn to the intersection of ethics and technology. We delved into global AI regulations, a topic close to my heart as I worked on regulatory compliance in a different field.

Beyond professional discussions, Archana's stories of philanthropy ignited a spark in me, inspiring me to explore new avenues of giving back.

As our mentorship journey unfolded, we discovered the power of collaboration and mutual learning. With each session, we embarked on a quest for growth and understanding, fueled by our shared passions and individual experiences.

Though our journey had just begun, the possibilities seemed endless. Together, Archana and I embraced the adventure, eager to see where it would take us.

Exploring AI career paths for non technical profiles

One week later, our journey through the realm of AI ethics continued. Picture our second session as a continuation of our journey through the realm of AI ethics. This time, we embarked on a quest to uncover how non-technical backgrounds can make significant contributions to the AI world. We explored the essence of "non-technical," focusing on roles in operations, partnerships, and regulatory compliance. Our shared passion for Ethical AI drove our discussion, despite our lack of expertise in policy-making or ethics.

Our conversation centered around comparing two key roles in the AI landscape: AI Ethicists and AI Product Managers.

AI Ethicists typically come from backgrounds in Ethics Research and Policy Making. They act as consultants, guiding engineers in developing AI models from an ethical perspective and providing recommendations. While they bring specific expertise, their focus is less on product development and more on continuous research.

On the other hand, AI Product Managers play a vital role in product development. They are not completely detached from AI ethics discussions, as they also consider ethical principles in their work. Their role involves facilitating collaboration between engineering, policy makers, and other cross-functional teams. They bring valuable insights into user needs, local market requirements, and pain points, ensuring AI aligns with business goals.

We found that AI Product Management offers the most crossover in skill sets, allowing for a holistic view of how AI integrates into daily business operations.

During our session, we discussed recommended trainings, including AI Product Management courses, Data Product Management nano degrees, and ethics certifications. For those considering the AI Product Management route, we emphasized the importance of studying AI product case studies, networking with product managers, and actively engaging in market analysis and proposing new features to product teams.

This discussion brought clarity to potential paths for non-technical profiles. It helped define potential strategies for further studies and identify roles and projects better aligned with my expertise and trajectory.

In summary, this session was a valuable step in shaping our journey in the AI space, providing clarity on where to focus our efforts and how to leverage our strengths in this evolving field.

Embracing Imposter Syndrome and Authenticity: Unveiling Layers

This session marked a significant shift in our mentorship journey, delving into deeper, more personal topics beyond the realms of AI and professional development. While the previous sessions were undeniably valuable in exploring mentorship matches and career guidance, our third meeting ushered in a new level of introspection.

We embarked on a candid discussion about imposter syndrome and how it has influenced our career paths, as well as the strategies we've employed to confront it. What resonated deeply with me was Archana's approach, which diverged from the conventional "fake it until you make it" mantra and instead emphasized curiosity, authenticity, and a commitment to continuous learning.

Curiosity and Authenticity: Archana advocated for replacing self-doubt with curiosity, shifting the focus from insecurity to the potential for growth and learning. This shift naturally aligns with one's authentic self, eliminating the need to mimic others' styles or narratives.

Life as a Project: Drawing from her background in data science, Archana likened life to a project, with experiences serving as valuable data points. Viewing life through this lens facilitates informed decision-making and encourages embracing opportunities for growth, even when they seem daunting.

Selfish or Selfless Decisions? Archana posed a thought-provoking question: If your purpose is altruistic, will your decisions reflect selflessness? This perspective empowers individuals to step outside their comfort zones in pursuit of their purpose, fostering a sense of excitement despite the accompanying discomfort.

The main lesson we took away from this session is that adopting a mindset centered on learning, exploration, and understanding our personal values can boost our confidence, help us overcome imposter syndrome, and unlock new opportunities as we embrace our multifaceted identity.

Archana urged me to address imposter syndrome, particularly in openly sharing personal stories, a step I've been reluctant to take. Her perspective, influenced by yogic science, resonated with my values and pushed me towards personal growth.

She highlighted two key reasons why sharing experiences shouldn't be feared:

Intention of Sharing: Keeping insights to ourselves limits their reach. Sharing allows for feedback and growth, benefiting everyone involved.

Community Building: Sharing fosters a sense of belonging and strengthens community bonds. It promotes support and the exchange of wisdom from diverse experiences.

And with these compelling reasons in mind, we were motivated to collaborate on documenting this transformative journey.

Generative AI: Balancing Originality with Responsible Use

I remember having this thought many years ago, long before generative AI was a reality: I have these 3 ingredients available for dinner. I wish internet could provide me a couple of recipe ideas and, in an ideal world, inform me of the nutritional gain and additional meal options I would get from adding a 4th ingredient. That would not only help me decide if a visit to the supermarket would be with my time, but also contribute to a zero waste approach to my meal prep. This is now possible with just a few questions asked to generative AI, that will probably get more sophisticated as a chef with time.

In our journey to document our mentorship experience through blogging, Archana and I pondered on the ethical use of generative AI. Our aim was to maintain authenticity while leveraging AI to refine our message, making it more accessible to diverse audiences.

Our primary use of generative AI centered on fine-tuning our messages, which led to some surprising benefits:

1. Strengthened Critical Thinking: Through AI prompts, we sharpened our critical thinking skills and gained deeper insights into the intent behind our messages. As we engaged more with the tool, refining and adjusting, our final message became clearer and more impactful.
2. Immediate Application of Insights: Over time, we seamlessly incorporated AI-learned insights into our initial drafts, witnessing noticeable improvements in our writing even before seeking AI assistance for review.

Subsequent mentorship sessions revealed further advantages of responsible generative AI use:

• Enhancing Thought Clarity and Diverse Thinking: Generative AI serves as an invaluable assistant for challenging our thoughts from multiple perspectives, fostering diverse thinking within a short timeframe.

Example: Here are my thoughts on topic “X”. What would a subject matter expert on “Y” provide as contradictory or supportive perspectives?

• Supporting Market Research: While validation is necessary, generative AI offers quick summaries of industry landscapes and competition analysis, tailored to specific requirements, aiding in informed decision-making.

Example: Act as if you were a product manager providing market insight on “X”, with high level pros and cons of available solutions and a table comparison including market reach, platform reach, pricing, usability, key features available and link to key sources and use cases.

• Facilitating Empathy and User Research: Generative AI facilitates understanding customer perspectives by predicting potential frustrations and expectations, enabling tailored product development.

Example: Based on this user persona [provide details], what are their key challenges when interacting with this product? List down the ideal features they would fix these issues and enhance their product experience.

• Streamlining Project Briefings: By clarifying goals and expectations tailored to the target audience, generative AI can help streamline communication, minimizing misunderstandings and enhancing collaboration.

Example: Rewrite this project briefing to audience A, to ensure goal B is met by timeline C, addressing challenges like D, E and F.?

Through exploring these diverse applications, we can harness the full potential of generative AI for critical thinking, stronger communication, creativity, collaboration and operational excellence, while keeping an active, critical and ethical approach.

Exploring Responsible AI in Healthcare: Ethical Considerations and Risks

In our last mentoring sessions, we navigated through the complex ethical aspects of AI. We began by looking at various industries before focusing on healthcare and education, exploring their unique ethical challenges. We explored the ethical considerations inherent in the ideation stage before narrowing our focus to delve deeper into specific sectors like healthcare and education.

The discussion commenced with a focus on the foundational pillars of ethical AI: transparency, privacy, accountability, and bias. Across all sectors, it's imperative to foster inclusive ideation processes, ensuring diverse perspectives are considered from the outset. We deliberated on the ethical responsibilities of AI creators, emphasizing the imperative to prioritize fairness, transparency, and accountability throughout the development lifecycle, starting by responsibly defining the product’s scope.

Transitioning our focus to healthcare, we confronted the unique ethical challenges embedded within this highly regulated domain. Here, stringent regulations dictate data privacy and access to sensitive information. Notable US regulations such as CMS guidelines and HIPAA Privacy Rule underscore the critical importance of clear, equitable data modeling. Ethical AI practices in healthcare should adhere to CMS standards to guarantee patient safety, equitable access to healthcare services, and high-quality care. These considerations should be prioritized from the earliest stages of ideation.

Within the realm of healthcare AI, two predominant ethical approaches emerged:

• Data-Centric Approach: Prioritizes data privacy and fairness to ensure inclusivity and mitigate biases.
• Modeling-Centric Approach: Emphasizes model explainability and accuracy - Transparent presentation of model parameters to stakeholders -Rigorous consideration of confusion metrics and error types to minimize false positives and false negatives. - Strategic feature prioritization to uphold fairness and equity in AI modeling.

Furthermore, we explored the evolving role of AI ethicists, who play a vital role in shaping ethical frameworks. They're crucial in developing ethical guidelines, but their involvement tends to decrease as projects move forward. Typically, they're not involved in ongoing ethical discussions or product ad

As we venture into the evolving landscape of healthcare AI, it's essential to stay firmly dedicated to ethical principles, paving the way for responsible innovation and fair healthcare outcomes.

Empowering Education: Navigating Responsible AI in Learning Environments

To conclude our mentorship journey exploring Responsible AI in Education, we delved into the unique aspects of applying AI in the educational realm. We examined not only its implications for responsible AI practices but also its potential to revolutionize personalized learning experiences.

We began by analyzing how AI can enhance user experience in education. By leveraging AI-driven personalization, educational platforms can address individual learning challenges and foster student-centric experiences. This involves employing conversational AI to engage students and incorporating personalized gamification and reward systems to boost motivation. Additionally, predictive models based on historical performance can offer insights to guide students towards achieving their learning goals.

On the operational front, AI offers streamlined solutions to alleviate teachers' administrative burdens and mitigate resource constraints. AI technologies can automate routine tasks such as lesson plan preparation, grading, and assessment, freeing up valuable time for educators to focus on personalized teaching and intervention strategies tailored to each student's needs. By analyzing student data, AI can generate personalized learning plans to optimize individual performance and facilitate targeted interventions for struggling students.

Similarly to our previous discussions unrelated to the education sector, prioritizing responsible ideation should be a primary focus. This involves carefully considering the intended impact of the product on end-users and all relevant stakeholders. By placing emphasis on responsible ideation, teams can proactively address potential ethical dilemmas, fostering trust and maximizing the positive impact of AI in education.

Other ethical considerations remain paramount in the education sector. To mitigate bias, datasets must be comprehensive, representing diverse student populations to prevent discriminatory outcomes. Transparency is also a critical pillar for AI in Education, to ensure stakeholders understand how AI algorithms assess skills and predict future performance, promoting accountability. Meanwhile, accuracy is crucial for conversational AI and scoring systems, ensuring reliable learning experiences. While privacy concerns are less pronounced in education compared to healthcare, building trust among parents regarding data privacy remains pivotal.?

Collaboration across sectors, as advocated in the "Manifesto for a Pro-Actively Responsible AI in Education," is vital for navigating the ethical, social, and cultural implications of AI. Engaging educators, policymakers, researchers, and technology developers fosters interdisciplinary dialogue and drives the co-creation of solutions aligned with diverse community needs. Moreover, promoting education about Responsible AI is essential for empowering stakeholders to make informed decisions and advocate for ethical AI practices.?

Equipping educators, students, and other stakeholders with the necessary knowledge and skills enables critical evaluation of AI systems and fosters responsible AI implementation in education.