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Articles by Aaron Burciaga
Council Post: How To Build Responsible AI, Step 4: Transparency
Share to Facebook Share to Twitter Share to Linkedin Aaron Burciaga is co-founder, chairman and CEO of DataPrime. getty Artificial intelligence is now woven into the fabric of our everyday lives. AI contributes to decisions we make as individuals, organizations, systems and economies—and if we wield it irresponsibly, we can cause serious harm.
Council Post: How To Build Responsible AI, Step 3: Resilience
Senior Practice Manager at Amazon Web Services (AWS), roles have included AI startup co-founder, VP Data & AI at ECS, Booz Allen, Accenture. getty Artificial intelligence is now an integral component of the processes and systems that drive our organizations. As AI practitioners, we must be intentional about developing, deploying and managing responsible AI — minimizing risk and removing bias while working toward our objectives.
Council Post: How To Build Responsible AI, Step 2: Impartiality
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. getty As the influence of artificial intelligence grows, it is increasingly vital to design processes and systems to harness AI while counterbalancing risk. Our charge is to eliminate bias, codify objectives and represent values. Responsible AI ensures alignment to our standards spanning data, algorithms, operations, technology and Human Computer Interaction.
Council Post: How To Build Responsible AI, Step 1: Accountability
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture getty The development, deployment and operation of irresponsible AI has done, and will continue to do, significant damage to individuals, business, markets, societies and economies of every scale. Now is the time to be explicit in the processes and systems that we create.
Council Post: Six Essential Elements Of A Responsible AI Model
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. Getty New ethical and moral questions continue to emerge as we expand how we use artificial intelligence in business and government. This is undoubtedly a good thing. Developing new technologies without incorporating ethics, morals or values would be careless at best, catastrophic at worst. This is also a gray area.
Web Articles - MCA
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Council Post: Scaling Blue-Collar AI Ethically And Sustainably
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. The United States is in a high-stakes global race to lead and innovate in artificial intelligence, and we can’t compete without making immediate, sweeping changes to our manpower management model.
ECS VP Aaron Burciaga on 'Responsible' AI Tech Development
TYSONS CORNER, VA, June 14, 2021 - Aaron Burciaga, vice president of artificial intelligence at ECS, said a responsible approach to AI product development could encourage federal agencies to adopt the technology for data science applications, ExecutiveBiz reported May 21.
Council Post: How To Advance Blue-Collar AI: Eight Actions For Citizens, Governments And Employers
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. By 2025, the AI market is expected to reach $390 billion, which represents a massive growth in demand over a short period of time. To keep up with rapidly evolving technologies, jobs and organizational needs, we have to recruit and train AI talent in new and innovative ways.
Aaron Daniel Burciaga's Page
Five Core Virtues For Data Science And Artificial Intelligence Posted on March 30, 2020 at 7:10am 0 Comments 1 Like Virtues should be digitized. As we speed toward reliance on machines to process more and more information and provide cognitive support for all types of decision-making, we must consider ways to imbue automated processes, data machinations and recommender systems with a sense of some of the finest human virtues.
Council Post: Why We Need A Blue-Collar AI Workforce
VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture.
Five Core Virtues For Data Science And Artificial Intelligence
Photo: Getty Virtues should be digitized. As we speed toward reliance on machines to process more and more information and provide cognitive support for all types of decision-making, we must consider ways to imbue automated processes, data machinations and recommender systems with a sense of some of the finest human virtues.
(Ethically) Experiment. Fail. Learn. Repeat.
Many projects and programs run afoul when well-intentioned folks allow speed, excitement, deadlines or other business objectives to skew what's considered to be a science routine that should've been conducted in a more disciplined fashion rather than expedited under a false pretense of agility. I have seen "agile methods" lead to devastating results when teams have misunderstood or overlooked the "why" and "how" regarding expertly applying agile methods.
Drive Better Outcomes By Combining The Strengths Of Data And CX
At first glance, data science and customer experience (CX) don’t have much in common. Data scientists like me are typical left-brained thinkers: detail-oriented, logical and quantitative. We want to study, process and analyze data to extract insights from it. Customer experience experts, on the other hand, gravitate toward a more intuitive and creative right-brained style. They strive to understand personas, identify patterns and use design-led thinking to improve the overall customer journey.
On Foxes, Pigeons And Groundhogs: The Case For Modern Microservice Applications
When trying to implement informed business solutions, what you see is all there is (WYSIATI), which can result in being “data-nearsighted.” For this reason, it is crucial to have a comprehensive data strategy in order to make better business decisions and implement efficient business solutions that use internal, external, real-time and historical data. Companies collect, store and manage (or rather, mismanage) data to develop decision making information.
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