AITech Interview with Colin Levy, Director of Legal at Malbek

Colin, could you elaborate on the concerns you’ve raised regarding AI’s impact on elections?

Answer: When it comes to AI and its impact/role in elections, the challenge is misinformation, generated by deep fakes (e.g. someone’s image and voice being used to propagate false opinions and incorrect information), bot accounts on social media propagating incorrect and/or misleading information and people’s susceptibility these types of behaviors. In practical terms this means that we all need to be more skeptical of what we see, read, and encounter online and be able to verify what we see and hear online.

How does AI contribute to the dissemination of misinformation and disinformation during electoral processes, in your view?

Answer: AI contributes to the dissemination of misinformation and disinformation by enabling the creation and spread of convincing fake content, such as deepfakes, and by personalizing and optimizing the delivery of content on social media platforms. These capabilities can be exploited to create false narratives, impersonate public figures, and undermine trust in the electoral process.

Can you provide examples of how AI technologies, such as deepfakes and social media manipulation, undermine the integrity of elections?

Deepfakes: AI-generated videos or audio recordings that convincingly depict real people saying or doing things they never did, which can be used to create false impressions of candidates or mislead about their positions.

Social Media Manipulation: The use of bots and algorithms to amplify divisive content, spread falsehoods, and manipulate trending topics to influence political discourse.

Personalized ads:The creation and use of political ads designed to mislead, convince others of false information, and/or get them to take actions that may be against their best interests and benefit someone else unbeknownst to the viewer of the ad.

What specific measures do you recommend to combat the threat of AI interference in elections?

Answer: I do not pretend or purport to have all the answers or even any answers, per se. What I can suggest is that measures including developing and enforcing strict regulations on political advertising and the use of personal data for political purposes, implementing robust and verifiable fact-checking and content verification mechanisms to identify and label or remove false information, and encouraging the development of AI systems that prioritize transparency, accountability, and the detection of manipulative content may be useful.

In your opinion, how can transparency and accountability in AI algorithms help prevent their misuse in the electoral context?

Answer: Enhancing transparency involves making the workings of AI algorithms more understandable and accessible to regulators and the public, including disclosing when and how AI is used in content curation and distribution. Accountability measures include holding platforms and creators legally and ethically responsible for the content disseminated by their AI systems so as to ensure that there are mechanisms to challenge and rectify misleading or harmful outputs.

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Confronting Workplace Anxiety with AI-powered Mental Health Strategies

In this demanding work environment, employee mental health is gradually becoming an important subject of focus for organizations. Therefore, it is essential to create a positive environment beyond personal satisfaction, which directly influences overall productivity, success, and engagement in the workplace.

Looking at the current scenario, addressing mental health assistance for employees is not merely an ethical duty for employers but also a fundamental factor in designing a happy and productive team. This is where AI-powered answers all your questions by playing the virtual assistant role regarding mental health.

In today’s exclusive AI Tech Park article, we will show how AI can be an influential supporter in promoting a healthier and more supportive work environment.

Role of AI in the Workplace for Employees Mental Wellbeing

According to a government-backed Labour Force Survey, it was observed that the total number of issues of work-related stress, depression, or anxiety was registered by around 602,000 employees, resulting in a loss of 12.8 million working days. This disengagement among employees resulted in companies losing more than $1 trillion in revenue each year.

Therefore, with AI, employers can open new paths for improving mental health and well-being in the workplace. When used wisely, AI offers valuable tools and aids to sustain employees by controlling stress and anxiety and preserving a positive work-life balance.

For instance, the new-age AI-powered assistants can be used to communicate with employees, offering support and guidance whenever needed, as these can detect signs of mental health distress and proactively reach out to assist those dealing with mental block or depression.

There is no doubt that AI in the workplace is here to stay, and if implemented adequately and with a positive intention, it could play a huge role in employees’ mental health. In the coming years, there might be obstacles related to privacy concerns, but AI will also ensure that workers are comfortable at their workplace and will accept various monitoring levels. Therefore, for a seamless AI interaction, it is important to adhere to workplace protocols to ensure that this technology is safe and relevant for employees with a proper understanding of its dos and don’ts.

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The Crucial Role of Algorithm Auditors in Algorithm Detection and Mitigation

In our increasingly data-driven world, algorithms play a significant role in shaping our lives. From loan approvals to social media feeds, these complex programs make decisions that can have a profound impact. However, algorithms are not infallible, and their development can be susceptible to biases. This is where algorithm auditors step in, acting as crucial watchdogs to ensure fairness and mitigate potential harm.

Algorithm auditors possess a unique skillset. They understand the intricacies of artificial intelligence (AI) and machine learning (ML), the technologies that power algorithms. But their expertise extends beyond technical knowledge. Auditors are also well-versed in ethics and fairness principles, allowing them to identify biases that might creep into the data or the algorithms themselves.

With the use of algorithms becoming widespread, the potential for algorithm bias has also impacted numerous decision-making processes, which is a growing concern in the IT sector.

The phenomenon of algorithm bias starts when the algorithms generate results that are systematically and unfairly skewed towards or against certain groups of people. This can have serious consequences, such as race discrimination, gender inequality, and the development of unfair disadvantages or advantages among citizens.

Therefore, to address this concern, the role of algorithm bias auditors has emerged, who are responsible for evaluating algorithms and their outputs to detect any biases that could impact decision-making.

In this exclusive AI TechPark article, we will comprehend the concept of algorithm bias and acknowledge the role of algorithm bias auditors in detecting algorithm bias.

The Role of Algorithm Auditors to Detect Algorithm Bias

According to a global survey, it has been witnessed that more than 56% of CIOs face issues related to the black box, algorithm bias, and privacy protection that create an adverse effect on citizens. Looking at these concerns, along with data privacy issues, IT organizations acknowledge the need for the role of an algorithm auditor.

Algorithm auditors play an essential role in ensuring that algorithms are unbiased and fair; therefore, they have to have a good understanding of ethics and fairness in artificial intelligence (AI) and machine learning (ML), along with practical knowledge of how algorithms can impact the lives of common people. They need to collaborate with developers and data scientists to review algorithms and ensure that they are fair, transparent, and explainable.

Algorithm auditors also use numerous tools to identify the factors associated with AI and ML algorithms’ results and understand the underlying data that has inherent algorithm bias. They can further execute periodical reviews to determine the fairness of the model after it is deployed in the real world. In addition to recognizing the problems, algorithm auditors also provide recommendations on how to make the model more ethical and explainable by implementing ethical frameworks.

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War Against AI: How to Reconcile Lawsuits and Public Backlash

In the rapidly evolving landscape of artificial intelligence (AI), media companies and other businesses alike continue to find themselves entangled in a web of lawsuits and public criticism, shining a spotlight on the issue of ethical transparency. Journalism has long been plagued by issues around deception — consumers often wonder what’s sensationalism and what’s not. However, with the latest casualty in the ongoing Sports Illustrated debacle, whose reputation greatly suffered after being accused of employing non-existent authors for AI-generated articles, a new fear among consumers was unlocked. Can consumers trust even the most renowned organizations to leverage AI effectively?

To further illustrate AI’s negative implications, early last year Gannett faced similar scrutiny when its AI experiment took an unexpected turn. Previously, the newspaper chain used AI  to write high school sports dispatches, however, the technology proved to be more harmful than helpful after it made several major mistakes in articles. The newspaper laid off part of its workforce, which was likely in hopes AI could replace human workers.

Meaningful Change Starts at The Top

It’s clear the future of AI will face a negative outlook without meaningful change. This change begins at the corporate level where organizations play a key role in shaping ethical practices around AI usage and trickles down to the employees who leverage it. As with most facets of business, change begins at the top of the organization.

In the case of AI, companies must not only prioritize the responsible integration of AI but also foster a culture that values ethical considerations (AI and any other endeavor), accountability, and transparency. By committing to these principles, leadership, and C-level executives set the tone for a transformative shift that acknowledges both the positive and negative impact of AI technologies.

To avoid any potential mishaps, workforce training should be set in place and revisited at a regular cadence to empower employees with the knowledge and skills necessary to combat the ethical complexities of AI.

However, change doesn’t stop at leadership; it also relates to the employees who use AI tools. Employees should be equipped with the knowledge and skills necessary to navigate ethical considerations. This includes understanding the limitations and biases as well as learning from the mistakes of others who’ve experienced negative implications using AI technologies, such as the organizations previously aforementioned.

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AI Ethics: A Boardroom Imperative

Artificial intelligence (AI) has been a game changer in the business landscape, as this technology can analyze massive amounts of data, make accurate predictions, and automate the business process.

However, AI and ethics problems have been in the picture for the past few years and are gradually increasing as AI becomes more pervasive. Therefore, the need of the hour is for chief information officers (CIOs) to be more vigilant and cognizant of ethical issues and find ways to eliminate or reduce bias.

Before proceeding further, let us understand the source challenge of AI. It has been witnessed that the data sets that AI algorithms consume to make informed decisions are considered to be biased around race and gender when applied to the healthcare industry, or the BFSI industry. Therefore, the CIOs and their teams need to focus on the data inputs, ensuring that the data sets are accurate, free from bias, and fair for all.

Thus, to make sure that the data IT professionals use and implement in the software meet all the requirements to build trustworthy systems and adopt a process-driven approach to ensure non-bais AI systems

This article aims to provide an overview of AI ethics, the impact of AI on CIOs, and their role in the business landscape.

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

The foundation of ethical AI responsibility is to develop a robust AI lifecycle. CIOs can establish ethical guidelines that merge with the internal standards applicable to developing AI systems and further ensure legal compliance from the outset. AI professionals and companies misidentify the applicable laws, regulations, and on-duty standards that guide the development process.

Conducting Assessments

Before commencing any AI development, companies should conduct a thorough assessment to identify biases, potential risks, and ethical implications associated with developing AI systems. IT professionals should actively participate in evaluating how AI systems can impact individuals’ autonomy, fairness, privacy, and transparency, while also keeping in mind human rights laws. The assessments result in a combined guide to strategically develop an AI lifecycle and a guide to mitigate AI challenges.

Data Collection and Pre-Processing Practice

To develop responsible and ethical AI, AI developers and CIOs must carefully check the data collection practices and ensure that the data is representative, unbiased, and diverse with minimal risk and no discriminatory outcomes. The preprocessing steps should focus on identifying and eliminating the biases that can be found while feeding the data into the system to ensure fairness when AI is making decisions.

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Buying Advice to Tackle AI Trust, Risk, and Security Management

In this technologically dominated era, the integration of artificial intelligence (AI) has become a trend in numerous industries across the globe. With this development of technology, AI brings potential risks like malicious attacks, data leakage, and tampering.

Thus, companies are going beyond traditional security measures and developing technology to secure AI applications and services and ensure they are ethical and secure. This revolutionary discipline and framework is known as AI Trust, Risk, and Security Management (AI TRiSM), which makes AI models reliable, trustworthy, private, and secure.

In this article, we will explore how chief information security officers (CISOs) can strategize an AI-TRiSM environment in the workplace.

Five Steps on How C-suite Can Promote Trustworthy AI in Their Organization 

The emergence of new technologies is likely to drive more potential risks; however, with the help of these five essential steps, CISOs and their teams can promote AI TRiSM solutions:

Defining AI Trust Across Different Departments

At its core, AI trust is the confidence that employees and other stakeholders have in a company that governs its digital assets. AI trust is driven by data accessibility, transparency, reliability, security, privacy, control, ethics, and responsibility. A CISO’s role is to educate employees on the concept of AI trust and how it is established inside a company, which differs depending on the industry and stakeholders. 

Develop an AI trust framework that helps achieve your organization’s strategic goals, such as improving customer connections, maximizing operational excellence, and empowering business processes that are essential to your value proposition. Once built, implement methods for measuring and improving your AI trust performance over time.

Ensure a Collaborative Leadership Mindset

As IT organizations rely on technology for back-office operations and customer-facing applications, IT leaders face the challenge of balancing business and technical risks, potentially leading to prioritizing one over the other.

CISOs and IT experts should evaluate the data risks and vulnerabilities that may exist in various business processes, such as finance, procurement, employee benefits, marketing, and other operations. For example, marketing and cybersecurity professionals might collaborate to determine what consumer data can be safely extracted, how it can be safeguarded, and how to communicate with customers accordingly.

As a CISO, you can adopt a federated model of accountability for AI trust that unites the C-suite around the common objective of seamless operation without hampering customers’ and organizations’ data. 

In conclusion, as businesses grapple with growing datasets and complicated regulatory environments, AI emerges as a powerful tool for overcoming these issues, ensuring efficiency and dependability in risk management and compliance. AI Trust, Risk, and Security Management (AI TRiSM) may assist businesses in protecting their AI applications and services from possible threats while ensuring they are utilized responsibly and compliantly.
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Paving the Path to Democratized Generative AI

Generative AI (GenAI) has the potential to automate a broad range of tasks, which boosts productivity, offers new opportunities, and reduces costs as it does not require technical skills to use generative AI tools or software and is widely available. Tech visionaries believe that GenAI will be accessible to workers worldwide to access information and skills across broader roles and business functions. This makes generative AI one of the most disruptive trends of this decade. According to Gartner, by 2026, more than 80% of companies will have employed generative AI APIs and models and implemented GenAI-enabled apps in production environments, compared to less than 5% in 2023.

In this article, we will explore what democratized generative AI is, how it works, and its current applications.

What is Democratized Generative AI?

Traditionally, artificial intelligence (AI) technologies were limited to technical experts; however, the growing availability of democratized generative AI marks the beginning of a paradigm shift in the technology landscape. The democratized GenAI aims to make AI technology more accessible to a wider range of audiences and focuses on providing user-friendly tools and platforms that allow users to create and interact with AI-powered models.

Democratization enables users from various fields, such as journalism, marketing, the arts, and others, to leverage AI algorithms and models to enhance their tasks and gain valuable insights from the given data.

Why Is Generative AI Democratization so Transformational?

At its core, generative AI democratization revolves around numerous data sources and insights from which numerous businesses and institutes can benefit. From decision-making in business to better public services in government sectors, generative AI reduces business costs by, for example, cutting expenditures and supporting development in working on important tasks.

Here are some specific areas where democratized generative AI can be transformational:

Reducing Entry Barriers

AI democratization reduces the entry barriers to using AI and machine learning (ML) algorithms for businesses and individuals so that they can use open-source datasets to train their AI models across any corner of the world without any financial investment.

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Unleashing the Power of AI for E-commerce Triumphs During the Holidays

For shoppers, this is the perfect time to celebrate the holiday season, and for the e-commerce sector, it is an ideal time to get many reviews. However, during this time of the year, E-commerce stores face problems with understaffed teams, supply chain woes, and changing customer behaviors, which, when combined, create the storm of the century.

As consumer behavior and preferences change every season, e-commerce merchants continuously seek innovative strategies to engage and satisfy their customers. With the help of artificial intelligence (AI), with its automation capabilities and data-driven insights, it can upscale this landscape and generate a higher conversion rate.

In this article, we will find out more about how e-commerce merchants can use AI to benefit themselves during this festive season.

Benefits of AI in E-commerce  

The benefits of artificial intelligence are immense, as it helps e-commerce companies provide personalized experiences to their target audience and enhance day-to-day operations effectively. Let’s explore each advantage carefully:

Personalized Shopping Experiences

AI can analyze your user’s behavior to provide more personalized recommendations to help customers discover products that align with their festive needs and preferences. It can also optimize target advertising campaigns, enabling retailers to customize their marketing efforts, resulting in a seamless and individual shopping experience for customers.

Reduced Operational Costs

Implementing AI in e-commerce allows your business to reduce costs through automation of tasks, which diminishes the need for manual labor. AI enables you to manage your inventory and automate customer interaction, leading to savings in operational costs, empowering businesses, and getting a clear picture of the marketing challenges.

Increased Sales Opportunities

AI in e-commerce aids in forecasting emerging market demand during the festive season, enabling online retailers to strategically introduce and position their products and estimate opportunities for cross-selling and upselling. This means your business can unlock new sales channels for efficient sales processes and broaden its market presence.

The Black Friday and Cyber Monday retail scene is a huge change, as online retailers have to make sure to adjust their operations as consumer behaviors and expectations evolve rapidly. So, to stay in the game of profitable e-commerce shops, they have to rely on artificial intelligence (AI) to improve efficiency, cut extra costs, and balance operations through automation and less human interaction.

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Overcoming the Barriers of the Physical World with AI

The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below.

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life.

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.

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Unlocking the Power of AI: Revolutionizing Data Management for Smarter Decision-Making

Artificial intelligence (AI) has revolutionized data management, empowering organizations to leverage data for informed decision-making.This article explores the transformative impact of AI in data management, presenting three key ways it enhances insights.

First, how AI automates critical processes, optimizing workflows and resource allocation. Second, how AI algorithms improve data quality by detecting and rectifying errors, ensuring reliable insights. Last, how AI enables businesses to make informed decisions by uncovering patterns and making accurate predictions.

Embracing AI in data management provides a competitive advantage, driving sophisticated decision-making and valuable insights across industries. This article will highlight the transformative potential of AI in data management, informing data decision-makers why it is essential to seize this opportunity for growth and success.

About the writer: With more than 20 years of experience in software engineering, Jay Mishra is an expert in product vision and development. Jay is the Chief Operating Officer for Astera Software, where he focuses on product development and strategic planning. Jay holds a Master of Science degree in Computer Science from Virginia Tech and a Bachelor of Science in Mathematics and Computing from the Indian Institute of Technology.

Data: it is the backbone of businesses, enabling informed decision-making, enhanced customer service, and innovation. However, effectively managing data presents challenges, from collection to storage and analysis.

Integrating unstructured data is a challenging task due to its diverse formats and lack of structure. Managing this type of data has historically required extensive manual labor and complex systems to ensure the data is properly extracted. Even with a team of experts, there is still a risk of human error, from missing fields to duplications and inconsistencies.

The rise of artificial intelligence (AI) is revolutionizing data management practices, ushering in a new era of efficiency and efficacy. Large language models such as ChatGPT, Bing, and Google Bard are transforming both the speed at which we can process data, and the way we can use and understand that data.

Just as the advent of Excel revolutionized data processing and analysis, AI represents a new frontier in data management capabilities. While Excel brought the power of spreadsheets to the masses, large language models harness the capabilities of advanced language models to process and analyze data in a conversational manner. Unlike Excel’s structured and formula-based approach, AI’s natural language processing abilities enable users to interact with data in a more intuitive and conversational manner.

Using AI, businesses can now query, explore, and gain insights from their data using everyday language, eliminating the need for complex formulas and technical expertise. This opens up new possibilities for users of all backgrounds to effortlessly leverage data in their decision-making processes.

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