Episode 6 of my podcast series, The BaDFun Podcast, is now live. Changing the focus to Enterprise-scale organizations, the title of this episode is “An agile approach to kick-starting data transformations, with Aaron Reich”. Here’s the blurb:
“Our guest for episode 6 of the BaDFun podcast is Aaron Reich, global lead for emerging technology at the global technology consulting firm Avanade. Tasked with guiding Avanade’s research and development 3-5 years into the future, while drawing from lessons learned by many of the largest companies in the world, Aaron is uniquely placed to understand the challenges Enterprises face when attempting to bring data solutions into production.”
“Aaron talks about stages of organizational readiness, overcoming the inertia of existing culture and methods, and an agile approach to kickstarting internal data platforms. He shares inspiring stories of spectacular early stage client successes on their roads to data transformation. He also dives deep into ongoing business and academic efforts to develop voluntary data ethics standards, and, gives us an overview of the principles and structures Avanade is developing as part of its own internal data ethics initiative.”
Episode 5 of my podcast series, The BaDFun Podcast, is now live. This time we focus on how business can use data to improve their customer connections. The title is “Finding the right business problems to solve using data, with Alex Brooks”. Here’s the blurb:
“In episode 5 of the BaDFun podcast we speak with Alex Brooks. Alex is founder, leader, and rainmaker at Entreprov, a Seattle-based team of two engineers, a data scientist and a back end developer who are using data to efficiently solve the kinds of problems many small to medium sized businesses are facing, like customer segmentation tools for marketing and engagement.”
“Alex speaks about the hurdle businesses face in finding not only the right mix of technology to solve business problems, but in finding the right mix of business problems to solve, while avoiding investing in products or services that are just passing fads—unless of course the plan is to cash-out after a trend dies.”
“We talk with Alex about the minimum quantity and quality of data necessary to deliver a solution like a recommendation engine for retailers, and the expertise gap many businesses are confronted with when the opportunity for using data and AI first arises. We also talk about why businesses should start now having a conversation with their customers about privacy and ethics, including what data they’re collecting, and what they plan to do with it, in order to set reasonable boundaries for customer data, “give the customer room”, and avoid creeping customers out.”
Episode 4 of my podcast series, The BaDFun Podcast, is now live. Moving to the entrepreneurial end of the spectrum this time, the title is “Democratizing Property Buying on a Startup Budget, with Ricardo Barrera”. Here’s the blurb:
“Our guest this week is startup founder Ricardo Barrera, a former Microsoft manager and experienced data scientist who at one time was responsible for optimizing the exabyte-scale data backbone of the world’s largest technology company. Now Ricardo is trying to democratize the way that real estate is purchased by automating many aspects of the property evaluation process. His approach raises the bar on traditional real estate valuation techniques by combining typical real estate data with photography and proprietary models that reveal explainable insights about properties, thereby enabling potential purchasers to make their own educated decisions, without relying on brokers.”
“During the course of our conversation Ricardo focuses on the challenges of creating a data-first direct-to-consumer business on a startup budget, including data access and quality issues, building a minimum viable team, rapidly prototyping potential products, and how to build trust in data models.”
Episode 3 of my new podcast series, The BaDFun Podcast, is now live. The title is “Embracing data science in healthcare, with Siddharth Mahapatra”. Here’s the blurb:
“This episode features Siddharth Mahapatra, director of master data management for one of the largest healthcare systems in the US. Siddharth speaks about how healthcare providers are embracing data science, in directions like
reducing waste by matching purchasing to predicted demand,
overcoming information hoarding, silos, and data quality issues,
making data accessible and trackable,
reconciling data sets that originate from different sources,
co-creating data solutions with end users to ensure adoption,
the importance of domain knowledge as well as data literacy in both managing and visualizing clinical data,
eliminating cultural biases in data capture and analysis and in patient experience,
and the responsibilities that accompany data ownership.”
If you work for an organization that uses data and artificial intelligence (AI), or if you are a consumer using data and AI-powered services, what do you need to know about data ethics?
Quite a bit, it turns out. The way things are going, it seems like every few days new ethics controversies, followed by new commitments to privacy and fairness, arise from the ways that businesses and government use data. A few examples:
• Voice assistants like Amazon’s Alexa, Siri, and “Hey Google” are everywhere, on smart phones, computers, and smart speakers. Voice commands satisfy more and more of our needs without resorting to keyboards, touch screens, or call centers. But recently one such assistant, while listening in on a family’s private conversations, recorded a conversation without the family’s knowledge and emailed that recording to a family member’s employee.
Episode 2 of my new podcast series, The BaDFun Podcast, is now live. The title is “Transforming to a Data-Centric Culture, with Rebecca Goehner”. Here’s the blurb:
“This week our guest is Rebecca Goehner, a management consultant in the Pacific Northwest, with degrees in neuroscience and economics, who focuses on strategic development. Rebecca speaks about how native processes develop and grow within organizations, and how data can be used to augment and change the direction of these processes to make them more efficient and effective. She dives into details with an example of how data can be used to simplify and accelerate sales pipelines across all sales channels. She advocates for starting small and moving one step at a time, and for the power of a growth mindset. A recurring theme is the importance of educating leadership about data’s potential to drive innovation, not only by adding net-new processes but by improving established processes as well.”
Deep learning systems, which are the most headline-grabbing examples of the AI revolution—beating the best human chess and poker players, self-driving cars, etc.—impress us so very much in part because they are inscrutable. Not even the designers of these systems know exactly why they make the decisions they make. We only know that they are capable of being highly accurate…on average.
Meanwhile, software companies are developing complex systems for business and government that rely on “secret sauce” proprietary data and AI models. In order to protect their intellectual property rights, and profitability, the developers of these systems typically decline to reveal how exactly their systems work. This gives rise to a tradeoff between profit motive, which enables rapid innovation (something government in particular isn’t known for), and transparency, which enables detection and correction of mistakes and biases. And mistakes do occur…on average.
On the one hand, a lack of transparency in deep learning and proprietary AI models has led to criticism from a number of sources. Organizations like AI Now and ProPublica are surfacing circumstances where a lack of transparency leads to abuses such as discriminatory bias. The EU has instituted regulations (namely GDPR) that guarantee its citizens the right to an appeal to a human being when AI-based decisions are being made. And, last but not least, there is growing awareness that AI systems—including autonomous driving and health care systems—can be invisibly manipulated by those with a motive like fraud or simple mischief. Continue reading “Who Needs Reasons for AI-Based Decisions?”