Spotlight on AI and Inclusivity: An Interview with Leondra Gonzalez

Spotlight on AI and Inclusivity: An Interview with Leondra Gonzalez was originally published on Ivy Exec.

In a rapidly evolving technological landscape, the governance and ethical deployment of artificial intelligence (AI) are critical concerns for enterprises worldwide. Leondra Gonzalez, Senior Data & Applied Scientist at Microsoft, brings a wealth of experience in navigating these complex issues.

In this interview, she shares insights on AI governance frameworks, strategies for enhancing AI literacy, responsible use of generative AI, optimizing enterprise AI strategies, promoting inclusivity in tech, and upcoming trends in AI technology.

 

AI Governance: Challenges and Opportunities

🔹 Question: What are the key challenges and opportunities in establishing effective AI governance frameworks within enterprises, and how has your experience at Microsoft influenced your views on this topic?

🔹 Leondra Gonzalez: The standardization and establishment of AI governance policies and principles is an endeavor that many institutions are facing as we speak. This includes industry, as well as academia and government. Thus, the main challenge is that there are no thoroughly tested and universally adopted enterprise AI governance practices. Furthermore, AI governance is a multi-disciplinary topic spanning the entire model development lifecycle, including but not limited to data acquisition, data privacy, security, ethics, algorithmic bias, transparency, interpretability, and much more.

This is further complicated by varying levels of data literacy and AI maturity throughout and within each organization. But while there are many challenges, there are also opportunities. There is a great opportunity for industry, academia and government to collaborate on AI governance theory, practicality, and regulation. Oftentimes, these institutions work in silos. Additionally, my time at Microsoft has shown me that the effective establishment and execution of internal Responsible AI policies and authorities is very possible.

 

Enhancing AI Literacy

🔹 Question: How can organizations improve AI literacy among their employees and the general public? Could you share insights from your work on tech education platforms and your recent technical text, Cracking the Data Science Interview?

🔹 Leondra Gonzalez: It is perhaps easier now than ever for organizations to adopt AI and data literacy agendas. The popularity and vast spectrum of applied AI has influenced a boom in technical education not only at universities, but also via bootcamps, workshops, certifications, MOOCs, consultants, and many more. 

Through my consulting business LRG Scientific LLC, I have collaborated with AI and data science platforms like DataCamp, SpringBoard, CodePath, and various others. This experience has taught me that everyone is hungry to learn more about AI, and I truly believe that such education should not be limited to those currently in tech or traditionally technical roles. 

Additionally, the explosion of LLMs has enabled the mass production of chatbots that can be used as education and AI literacy agents.

 

Generative AI Boom and Best Practices

🔹 Question: With the current surge in generative AI applications, what are some best practices for businesses to harness this technology responsibly and effectively, based on your research and professional experience?

🔹 Leondra Gonzalez: Organizations must first understand that AI is a tool, and like any other tool, it can and will be misused without the proper guidance. It is critical that organizations establish AI governance policies and, ideally, a centralized authoritative body whose sole purpose is to author, review, audit, and enforce said policies. Without these elements, organizations leave themselves vulnerable to a host of risks, including security incidents, unethical application of AI models, legal and regulatory infringement, technical debt, misappropriated budgets, unrealistic expectations, strategic misalignment, talent mismanagement – the list goes on and on.

 

Optimizing AI Strategies in Enterprise

🔹 Question: What tools, frameworks, and platforms are available to enterprises to address AI governance and responsible AI practices? Can you provide examples from your work at Microsoft?

🔹 Leondra Gonzalez: AI governance is an increasingly discussed and crucial component of AI development and commercialization that is yet to be fully solved. As of now, there are no universally accepted standards or practices, and legislation is still being written. Furthermore, regulations may differ by country or even state. However, there are some tools and frameworks that are emerging that customers may consider. Microsoft has a fairly comprehensive internal Responsible AI Standard framework, and offers tools and guidelines to help implement them. Microsoft also published an AI Impact Assessment template which details the program’s system information, lifecycle, purpose, features, deployment details, stakeholders and communications, potential risks / harms and benefits, human oversight, AI interaction and human roles, fairness, complexity, restricted or unsupported uses, limitations, data requirements, transparency, mitigation practices, reliability and safeness, etc.

They also have a multi-component Responsible AI Toolbox package with resources for reporting model errors, interpretability of predictions, group fairness metrics, cohort analysis, mitigation opportunities, causal analysis, counterfactual analysis, and so much more. This is in addition to other Microsoft offerings like Purview for data governance and Azure AI Content Safety to name a few. There are also some out-of-the-box solutions offered by IBM Watson, Atlan, Credo AI, Fiddler AI, Fairly AI, Mind Foundry, Sigma Red AI, Solas AI, and Monitaur. There are also the MLOps platforms like AWS SageMaker, Datarobot, and Vertex AI. Some LLMOps tools have surfaced, like Akira AI, Calypso AI, and Arthur AI. However, the most popular LLM Orchestration and DevOps frameworks include LangChain, LlamaIndex, and LangSmith.

 

Promoting Inclusivity in Tech

🔹 Question: Inclusivity in tech is a passion of yours. What initiatives or strategies have you found most effective in making AI and tech more inclusive, and how do these initiatives reflect the values of the Ivy Exec community?

🔹 Leondra Gonzalez: This is a complex topic but in a nutshell, I’ve always found representation to be an excellent catalyst for inclusivity in tech. While I believe exposure is also important, I do not believe it is enough to tap into underrepresented communities – by which I mean every community possible, based on race, ethnicity, gender, age, nationality, region, class, educational backgrounds, values, disability, etc. Despite being exposed to (and even excelling in) technical topics in college, I did consider tech as a career until I ran into people with which I could relate.

Thus, I find it most effective for people to see themselves in technical roles so that they know it’s possible and accessible. Something that attracted me to Ivy Exec is its ability to cultivate accessibility by offering engaging discussions, courses, and coaching. Additionally, I strongly believe that one of the greatest benefits of an inclusive culture is the opportunity to collaborate with those from different perspectives. Ivy Exec facilitates events and discussions that achieve this opportunity.

 

Emerging AI Trends

🔹 Question: Looking ahead, what emerging AI trends do you believe will have the most significant impact on the technology landscape, and how can Ivy Exec’s network of professionals help businesses navigate these changes?

🔹 Leondra Gonzalez: That is the billion dollar question! I believe AI will become increasingly more popular among the average consumer by embedding AI into low or no-code applications. There has already been a massive explosion in AI applications over the past few years. Now, content production and information retrieval is accessible to pretty much anyone without requiring a single technical or artistic bone in your body. The sky is the limit, and I believe there is a limitless opportunity for companies to enter the market in producing AI applications that are accessible, affordable, and useful to the average person.

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