Sentient AI A year spent in artificial
Description: Sentient AI A year spent in artificial intelligence is enough to make one believe in God. The WashU Team Lei Song - JDMBA Candidate Operations Shashwat Anand - First Year MBA Sales Rufus Ayisi - First Year MBA Strategy Finance Divyam
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slide1. Sentient AI A year spent in artificial intelligence is enough to make one believe in God.<br>
slide2. The WashU Team Lei Song
-
JD/MBA Candidate Operations Shashwat Anand
-
First Year MBA Sales Rufus Ayisi
-
First Year MBA Strategy & Finance Divyam Jain
-
First Year MBA Strategy & Consulting<br>
slide3. AI is primarily giving a machine a capacity to process large amounts of data, and then produce results. The unique advantage is that we teach the machine to look for inputs. This creates the perception that the machine is "thinking".
In today's data rich world, the more we know about the needs of our stakeholders, the better we can support them. Knowing about your wife's favorite color can help me suggest a product which I know will have a higher chance of being bought. A business' appetite to know about its customer is limitless. This is a win-win situation, as a customer experiences higher customisation, and businesses enjoy loyalty. Background<br>
slide4. How It Works Complexity Overplayed<br>
slide5. Major Benefits of AI Automation, Hyper-personalization, and Risk reduction Efficiency and productivity gains Automate routine tasks, freeing up humans to focus on creative and strategic work
About half of America’s workforce feels their company’s AI deployment is outpacing the accuracy and productivity of comparable human activity
Minimize the costs associated with performing mundane, repeatable tasks Increase in computing power to simultaneously process volumes of data
Leveraging user data on demographics, behavior, and preferences, to create custom recommendations, content, and interactions
Providing customer what they want without them asking for it Predictive forms of AI to anticipate both how attacks will occur and what motivates them
Implement real-time monitoring capabilities to alert businesses on issues, recommend action, and, in some cases, even initiate a response Enhanced customer experience Improved monitoring and oversight<br>
slide6. Challenges EMPLOYEES
Lack of awareness and misconception about the impact of AI on their job
Skill Shortage CUSTOMERS
Customer Privacy
Ethical justification
Inaccurate data leading to biased outcomes for end customers STAKEHOLDERS
Vulnerability to human attacks and errors
Data issues
Provision for unforeseen consequences of AI
Surveillance and warfare<br>
slide7. Recommendations Standard practices - Setting up clear standards for AI model development and documentation
Break down Silos - Common AI and data policies across stakeholders in data science, legal, and governance teams
Independent reviews – Reviews and audits using independent standards set up by FTC to evaluate model fairness, transparency
Governance Framework – Creating a Centre of Excellence to build guard rails for the use of AI systems which can act as central monitoring department<br>
slide8. Joint efforts by regulatory bodies and businesses Governments Pursue a common objective with business:
Risk-based regulation system Promote Control Entry and Development business activities Specific Industry "threshold" Collaborate on Data, Communications: Produce a Risk-based entry evaluation approach together
e.g. DOC and Institue of Standards and Technologies co-work with lead tech institution Pre-monitor and post-action judgment
Interaction with consumer and business entities' activities
e.g., ome up with new ccriteria for monitoring price-fixing, and monopoly g via use of Algorithms Department of Drug Administration: specific "threshold" on biometrics development and activities with "super high" risk probability
Finance: monitoring security fraud and insider trading via data manipulation<br>
slide9. Business Entities: Collaborate, Partner, Compliance Collaborations
Talent & Industry Trend communication
Establish advisory committee composed of experts and industry leader
Private Industry Association
Association standard and risk-management criteria
A higher threshold showing industry commitment
Partner
Partner with governments on promoting data security and information privacy
platform, security system built-up projects
Work with DOC
Compliance
Comply with the previously mentioned Algorithms test process, trustful action and forbid manipulation of data
Value and commitment
Setup Internal AI Audit team
comply with disclosure and monitor appropriate use for the intended purpose
Appoint Chief Data Ethics Officer:
Develop AI policies with commitment to safety, fairness, diversity, and privacy
Comply with FTC monitoring and regulation<br>
slide10. Thank You! We appreciate your time and attention. Team WashU<br>
slide2. The WashU Team Lei Song
-
JD/MBA Candidate Operations Shashwat Anand
-
First Year MBA Sales Rufus Ayisi
-
First Year MBA Strategy & Finance Divyam Jain
-
First Year MBA Strategy & Consulting<br>
slide3. AI is primarily giving a machine a capacity to process large amounts of data, and then produce results. The unique advantage is that we teach the machine to look for inputs. This creates the perception that the machine is "thinking".
In today's data rich world, the more we know about the needs of our stakeholders, the better we can support them. Knowing about your wife's favorite color can help me suggest a product which I know will have a higher chance of being bought. A business' appetite to know about its customer is limitless. This is a win-win situation, as a customer experiences higher customisation, and businesses enjoy loyalty. Background<br>
slide4. How It Works Complexity Overplayed<br>
slide5. Major Benefits of AI Automation, Hyper-personalization, and Risk reduction Efficiency and productivity gains Automate routine tasks, freeing up humans to focus on creative and strategic work
About half of America’s workforce feels their company’s AI deployment is outpacing the accuracy and productivity of comparable human activity
Minimize the costs associated with performing mundane, repeatable tasks Increase in computing power to simultaneously process volumes of data
Leveraging user data on demographics, behavior, and preferences, to create custom recommendations, content, and interactions
Providing customer what they want without them asking for it Predictive forms of AI to anticipate both how attacks will occur and what motivates them
Implement real-time monitoring capabilities to alert businesses on issues, recommend action, and, in some cases, even initiate a response Enhanced customer experience Improved monitoring and oversight<br>
slide6. Challenges EMPLOYEES
Lack of awareness and misconception about the impact of AI on their job
Skill Shortage CUSTOMERS
Customer Privacy
Ethical justification
Inaccurate data leading to biased outcomes for end customers STAKEHOLDERS
Vulnerability to human attacks and errors
Data issues
Provision for unforeseen consequences of AI
Surveillance and warfare<br>
slide7. Recommendations Standard practices - Setting up clear standards for AI model development and documentation
Break down Silos - Common AI and data policies across stakeholders in data science, legal, and governance teams
Independent reviews – Reviews and audits using independent standards set up by FTC to evaluate model fairness, transparency
Governance Framework – Creating a Centre of Excellence to build guard rails for the use of AI systems which can act as central monitoring department<br>
slide8. Joint efforts by regulatory bodies and businesses Governments Pursue a common objective with business:
Risk-based regulation system Promote Control Entry and Development business activities Specific Industry "threshold" Collaborate on Data, Communications: Produce a Risk-based entry evaluation approach together
e.g. DOC and Institue of Standards and Technologies co-work with lead tech institution Pre-monitor and post-action judgment
Interaction with consumer and business entities' activities
e.g., ome up with new ccriteria for monitoring price-fixing, and monopoly g via use of Algorithms Department of Drug Administration: specific "threshold" on biometrics development and activities with "super high" risk probability
Finance: monitoring security fraud and insider trading via data manipulation<br>
slide9. Business Entities: Collaborate, Partner, Compliance Collaborations
Talent & Industry Trend communication
Establish advisory committee composed of experts and industry leader
Private Industry Association
Association standard and risk-management criteria
A higher threshold showing industry commitment
Partner
Partner with governments on promoting data security and information privacy
platform, security system built-up projects
Work with DOC
Compliance
Comply with the previously mentioned Algorithms test process, trustful action and forbid manipulation of data
Value and commitment
Setup Internal AI Audit team
comply with disclosure and monitor appropriate use for the intended purpose
Appoint Chief Data Ethics Officer:
Develop AI policies with commitment to safety, fairness, diversity, and privacy
Comply with FTC monitoring and regulation<br>
slide10. Thank You! We appreciate your time and attention. Team WashU<br>