074 | Detecting AI

It’s the weekend and here is a short Saturday read.

Enjoy your time.

Let’s dig in.

A special welcome 👋 to my NEW SUBSCRIBERS! 

To read previous posts, go here.

In this edition we will

1. tackle 3 open issues for AI in the Enterprise

2. get 10 tools to identify AI generated content

3. learn what the Hiroshima Process is

4. find chatbots in the drive-thru

5. meme 10 US cities in the year 2050

Enterprise AI: Tackling the Triad of Challenges

Generative AI is fast becoming the bedrock of modern enterprise, fostering innovation and delivering previously unimaginable efficiencies. In legal, more teams are testing informally on a personal basis or more formally through IT sanctioned trials.

It is temping to consider what this tech cold do for knowledge intensive domains such as legal that have struggled with things such as knowledge management, enterprise search, and document management. Generative AI indeed has the potential to more fully address these demands/gaps than any other technology.

However, amidst this technological fervor, it is crucial to acknowledge and address three looming challenges: privacy concerns related to employee monitoring, security risks associated with proprietary data, and competitive exposure due to aggregated data insights.

1. Employee Monitoring: Privacy vs Productivity

Generative AI technologies, by their very nature, amass vast amounts of data during their operation. Within an enterprise, this data often encompasses detailed records of employee activities. While the potential to use this information for performance analysis is tantalizing, it raises critical questions about employee privacy and potential misuse. The risk lies not only in perceived surveillance but also in erroneous interpretations of data.

Consider a scenario where an employee who logs longer hours but with lower efficiency is deemed more valuable than a highly efficient peer working shorter hours. A focus on quantifiables like hours worked rather than qualitative outcomes could inadvertently lead to favoring inefficiency, potentially driving away valuable, productive employees.

This scenario is palpable in a profession like legal where the dominant economiv model relies on amassing and billing hours.

The solution to this quandary is rooted in transparent communication and consent. Employees should understand how their data is used – for what purposes, by whom, and what access they will have to any monitoring insight. 

2. Securing Proprietary Information: The Hidden Risk

The data collected by generative AI can be a Pandora's box. It may include sensitive details about proprietary issues, processes, internal operations, and decision-making methods. Falling into the wrong hands, these insights could prove disastrous, opening a company up to corporate espionage, intellectual property theft, or competitive disadvantages.

Those are the obvious things to consider but how about permission-based access. With generative AI it is still incredibly challenging to restrict how the AI model responds based on some user-based permission.

Securing this data is paramount, particularly considering the increasing prevalence of remote work, which could expose these critical data sets. Implementing robust data security protocols, coupled with ongoing employee training, is crucial to ensuring the continued integrity and competitiveness of a company.

3. Competitive Exposure: The Double-Edged Sword of Aggregated Data 

Aggregated data from across an organization provides valuable insights, which can lead to increased efficiency and profitability. However, this data could also be a ticking time bomb if accessed by hostile actors or even regulators.

In the wrong hands, detailed operational insights could be used to create a digital clone of the firm, enabling a competitor to anticipate and react to the organization’s strategies effectively. Alternatively, regulators or litigators could leverage this data as irrefutable evidence of wrongdoing. Thus, maintaining the confidentiality of aggregated data insights is just as crucial as ensuring individual data security.

Tool you can Use: 10 AI Detection Tools

While more and more of us are using AI, more of us are also thinking about how to tell the difference between AI and human-generated output.

Here are 10 tools you can use to learn what has been generated with AI and what hasn’t. I put a chart below to help you compare but it is a bit small.

1. AI Text Classifier

2. Content at Scale AI Detector

3. Copyleaks

4. Crossplag

5. GLTR

6. GPTZero

7. Hugging Face

8. Originality AI

9. Sapling.ai

10. Writer AI Content Detector

News you can Use: 

Navigating the Global AI Regulatory Frontier

As the dizzying pace of AI advancement speeds forward, the G-7 countries are expressing concern and uniting to establish governance in line with their shared values. Coined as the "Hiroshima Process," these discussions aim to curate a human-centric and trustworthy approach to AI development.

Full article here.

News you can Lose: 

Fast Food, Future Flavor: Wendy's Serves Up AI Chatbots at the Drive-Thru

Fast food is about to get a bit more futuristic as Wendy's, the fifth largest fast food chain in the U.S, plans to bring AI chatbots to its drive-thrus. Collaborating with Google, Wendy's aims to deploy a powerful Large Language Model called FreshAI, which promises to outperform their best customer service representative in interacting with customers.

In the Memetime: 

While these were not intended to be memes, they sorta look like some.

DISCLAIMER: None of this is legal advice. This newsletter is strictly educational and is not legal advice or a solicitation to buy or sell any assets or to make any legal decisions. Please /be careful and do your own research.8