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AI should be trained to respect a regulatory ‘constitution’ says BofE policy maker

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Innovative AI models should be trained to respect a ‘constitution’ or a set of regulatory rules that would reduce the risk of harmful behaviour, argues a senior Bank of England policy maker.
In a speech at CityWeek in London, Randall Kroszner, an external member of the Bank of England’s financial policy committee, outlined the distinction between fundamentally disruptive versus more incremental innovation and the different regulatory challenges posed.
“When innovation is incremental it is easier for regulators to understand the consequences of their actions and to do a reasonable job of undertaking regulatory actions that align with achieving their financial stability goals,” he says.
However, in the case of AI, innovation comes thick and fast, and is more likely to be a disruptive force, making it “much more difficult for regulators to know what actions to take to achieve their financial stability goals and what the unintended consequences could be for both stability and for growth and innovation.”
Kroszner suggests that the central bank’s up-and-coming Digital Securities Sandbox, that will allow firms to use developing technology, such as distributed ledger technology, in the issuance, trading and settlement of securities such as shares and bonds, may no longer be an applicable tool for dealing with artifical intelligence technology.
“Fundamentally disruptive innovations – such as ChatGPT and subsequent AI tools – often involve the potential for extraordinarily rapid scaling that test the limits of regulatory tools,” he notes. “In such a circumstance, a sandbox approach may not be applicable, and policymakers may themselves need to innovate further in the face of disruptive change.”
He points to a recent speech by FPC colleague Jon Hall that highlighted the potential risks emerging from neural networks becoming what he referred to as ‘deep trading agents’ and the potential for their incentives to become misaligned with that of regulators and the public good. This, he argued, could help amplify shocks and reduce market stability.
One proposal to mitigate this risk was to train neural networks to respect a ‘constitution’ or a set of regulatory rules.
Kroszner suggests that the idea of a ‘constitution’ could be combined with, and tested in, a sandbox as way of shepherding new innovation in a way that supports financial stability.
“In the cases where fundamentally disruptive change scales so rapidly that a sandbox approach may not be applicable, a ‘constitutional’ approach may be the most appropriate one to take,” he says.
Source: finextra.com
 
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Ethical considerations for AI adoption in MOps

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Understanding the ethical implications and best practices for responsibly integrating AI into marketing operations is crucial.
AI-driven tools and techniques offer significant benefits such as enhanced efficiency, personalized customer experiences, and data-driven decision-making. However, deploying AI in your organization requires meticulous adherence to ethical standards to uphold customer trust and facilitate a smooth transition for your workforce.
Here are key ethical considerations that marketing leaders must address when adopting AI, spanning enterprise governance, customer relations, and regulatory compliance:
Enterprise Considerations:

Transparency and Explainability: Customers must comprehend how their data influences AI-driven decisions. Ensure transparency in AI processes and provide explainability features to elucidate specific outcomes derived from AI algorithms.
Intellectual Property: Understand ownership and permissions related to AI tools trained on existing datasets. Given recent IP controversies, it’s essential to clarify data ownership chains. Whenever feasible, prioritize tools trained solely on your enterprise’s data to mitigate risks.
Compliance with Regulations: Adhere strictly to data privacy regulations such as GDPR and CCPA, along with emerging AI-specific regulations in regions like the EU. Stay informed about regulatory updates to align AI strategies accordingly.
Start Small and Scale: Initiate AI projects through pilot programs to assess effectiveness and gather feedback iteratively. This approach allows for adjustments and improvements before full deployment, enhancing the success of AI adoption.

Customer Considerations:

Acknowledgment of AI Usage: Disclose when AI powers customer interactions to maintain transparency. Offer options for human interaction alongside AI-driven communication to respect customer preferences.
Proactive Consent Management: Obtain explicit consent from customers regarding AI usage and data handling practices. Implement clear privacy policies and opt-in mechanisms to ensure compliance and uphold customer choices.
Bias Detection and Mitigation: Mitigate bias in AI models through regular audits and corrections. Collaborate with data and IT teams to ensure fairness in AI-driven processes, whether using off-the-shelf platforms or developing proprietary systems.
Data Anonymization: Protect customer privacy by anonymizing data used in AI training and operations. This ensures that AI models derive insights without compromising personally identifiable information.
Inclusivity in AI Applications: Embrace diverse perspectives and rigorous data scrutiny to promote ethical AI practices. Ensure AI applications, whether for personalization or predictive analytics, are inclusive and do not inadvertently exclude certain demographics.

Adopting AI in marketing operations holds immense potential for enhancing customer experiences and operational efficiency. However, prioritizing ethical considerations is paramount to safeguarding customer trust and ensuring regulatory compliance. By integrating these best practices, organizations can navigate the complexities of AI adoption responsibly and sustainably.
Source: martech.org
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From AI trainers to ethicists: AI may obsolete some jobs but generate new ones

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AI is rapidly taking over routine tasks in IT development and management, signaling potential benefits for the industry.
Recently, a job listing emerged for an “AI competency leader,” a position focused on collaborating across teams to implement generative artificial intelligence strategies across various domains. Such roles, unheard of just a year ago, are becoming commonplace in the AI era. While businesses are eager to harness AI’s potential, expertise in development and data science alone is no longer sufficient. The expanding responsibilities in AI initiatives span from algorithm training to ethical oversight.
Business landscapes are witnessing the emergence of novel job roles, as noted by industry insiders. Nearly seven in ten business leaders foresee the rise of generative AI leading to new positions such as AI auditors, ethicists, and prompt engineers, according to a Capgemini report. Doug Ross, Vice President and US Generative AI Leader at Sogeti, part of Capgemini, highlighted the growing demand for roles in AI management and digital transformation, emphasizing governance, strategy, stakeholder engagement, and policy in AI integration.
Robert Ghrist, Associate Dean at the University of Pennsylvania School of Engineering and Applied Sciences, distinguishes two categories of AI roles. The first encompasses AI specialists with comprehensive training in machine learning, neural networks, and large language models. The second category, more intertwined with broader business and managerial functions, involves roles like “AI plus X,” where X represents fields such as law, medicine, or education, demanding both AI expertise and industry-specific knowledge.
Prompt engineering has also emerged as a prominent role in the AI era. Tony Lee, CTO at Hyperscience, acknowledges its current demand but questions its future as a full-time profession, suggesting its evolution may hinge on technological advancements towards more conversational and human-like interfaces.
In summary, as AI reshapes IT operations, it brings forth a spectrum of new career opportunities ranging from specialized AI roles to interdisciplinary positions combining AI with diverse industries. The evolution of these roles will depend on ongoing technological advancements and the evolving needs of businesses adopting AI strategies.
Source: zdnet.com
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The effects of GenAI on tax firm rates

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Generative artificial intelligence (GenAI) is beginning to penetrate the tax profession, promising substantial transformations in operational practices.
Despite initial concerns among professionals regarding accuracy and potential misuse, there is widespread optimism about GenAI’s capacity to streamline workflows and reduce time spent on repetitive tasks.
According to the Thomson Reuters Institute’s 2024 Generative AI in Professional Services report, professionals view GenAI positively, foreseeing numerous current and future benefits for tax and accounting practices. By leveraging GenAI, tax firms and departments can potentially expand service offerings, particularly in advisory capacities, leading to significant shifts in pricing strategies.
Zach Warren, who led the report, discusses the current state of GenAI adoption within tax firms and its implications:
Question: How extensively have tax firms embraced GenAI so far?
Warren: Our survey indicates that only about 10% of tax firms have adopted generative AI extensively; it remains largely in the exploration phase. Historically, the tax sector has been cautious about embracing new technologies due to regulatory concerns and risk aversion. However, those who have implemented GenAI are utilizing it across various functions such as tax return preparation, client research, general accounting, bookkeeping, and compliance. Despite these advancements, discussions around GenAI in contractual contexts like RFPs are minimal, indicating early-stage adoption within the profession.
Question: How could GenAI benefit tax firms?
Warren: One of the primary challenges facing the tax profession is workforce shortages amidst increasing complexities. GenAI offers a pathway to achieve more with fewer resources, thereby enhancing operational efficiency. Specific regulatory challenges, like the forthcoming Pillar 2 Regulation, are expected to necessitate significant process changes, where GenAI can play a crucial role in ensuring compliance across global frameworks. Additionally, the shift towards electronic invoicing in many jurisdictions underscores the need for advanced data management solutions, where GenAI can aid in organizing and maintaining compliance.
Question: Do clients anticipate faster service and reduced fees through GenAI?
Warren: According to our report, a majority of corporate tax departments express expectations for their external firms to adopt GenAI technologies, anticipating enhanced efficiency and potentially lower costs. While clients are increasingly supportive of technology-driven efficiencies, the ultimate distribution of these benefits between clients and firms remains a critical consideration. Nonetheless, the consensus is growing that GenAI will be instrumental in meeting client demands for quicker turnarounds and cost-effectiveness in tax services.
In conclusion, while GenAI’s integration into tax practices is still in its early stages, its potential to revolutionize operations and meet evolving regulatory demands is gaining traction among professionals and clients alike. As technology continues to advance, tax firms are poised to leverage GenAI to navigate complexities and deliver enhanced value to their clients efficiently.
Source: tax.thomsonreuters.com

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