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In January, TIME revealed Microsoft-backed OpenAI outsourced workers in Kenya in late 2021 to moderate internet data, forming a fundamental part of developing generative AI sensation ChatGPTs safety system.

According to documents seen by the news outlet, moderators earned around $2 per hour to label texts which detailed injuries, sexual abuse and self-harm. These workers were also asked to collect images, some reportedly illegal under US law, in a separate project for OpenAIs image generator DALL-E.

In a statement to TIME, OpenAI explained it took the well-being of its contractors very seriously and that programmes offering support were available through the outsourcing company, which believed its employees did not request for support through the right channels.

The work involved was so traumatic that the company dealing with OpenAI to outsource the jobs cut short its contract with the AI powerhouse, a recent Wall Street Journal article indicated. Meanwhile, a growing body of research continues to reveal the dependence of big technology companies to conduct precious work in the global south as part of a mission to make AI safe.

Surveys conducted over the years also revealed General Purpose AI deployed in biometrics, policing and housing systems have already caused gender and racial discrimination.

As ChatGPT began to fully take off, the recent dismissal of Microsofts responsible AI team raised eyebrows and questions of whether ethical concerns are actually a priority in the multibillion-dollar AI economy.

That is not to say the technology sector as a whole is not taking the risks around generative AI seriously.

Major industry figures did indeed call for a pause in the technologys developments until a robust AI act is in place. However, researchers speaking to Mobile World Live (MWL) believe the public should look a little further beyond policymaking.

Abid Adonis, researcher at Oxford Internet Institute, argues the task of ensuring ethical AI needs to be expanded.

Now, we only see two powers: regulators and big tech, but we also have civil society and scholars. And its important to hear what marginalised groups say about this because its missing from the discussion.

False AIThis view resonates with Dr Alison Powell, associate professor in Media and Communications at the London School of Economics and Political Science and director of JustAI network at the Ada Lovelace Institute.

Powell told MWL the emphasis on artificial general intelligence which industry heavyweights claimed can eclipse humans cognitive abilities and therefore dominate job markets is already in itself harmful.

Its harmful because it focuses on an imagined world rather than the actual world we live in.

This is particularly reflected in Large Language Models (LLM) built on internet data. Powell pointed out that while there are a lot of languages spoken in the actual world, English is largely dominant on the internet.

In the world, there are many ways that people experience things, express ourselves and work together. Not all of these are present online.

Powell further warned about the hype around AIs decision-making abilities and suggested the technologys powers do not take into account social responsibilities.

This somewhat makes sense when considering the fact generative AI posterchild ChatGPT falsely accused law professor Jonathan Turley of assaulting a student and made up a story about the death of Alexander Hanff, a privacy technologist who helped craft GDPR.

Other examples include data-filtering practices in GPT-3, which used a classification system to automatically discard obscene and inappropriate material.

Further flaws in LLM were highlighted in a recent report by The Washington Post, which stated tech companies had grown secretive about what they feed the AI, such as using data from websites that could be deemed discriminatory.

This backed up a study from 2021, which found generative AI has the potential to amplify privileged views, pointing to GPT-2s training data extracted from Reddit, Twitter and Wikipedia, all of which have predominantly male users.

Cultural machinePowell stressed the need to understand the social aspects where technology is more likely to cause harm before considering how to make it more ethical.

AIs are institutional machines, theyre social machines and theyre cultural machines, she argued.

If were walking away from saying, How do we do this technically, in the gears? then we produce that double bind. But if we take a step back, then we notice all of these systems are institutional systems. Thinking about making systems work along the lines of justice and inclusion is about not how the machines work, but how institutions work.

Adonis added a nuanced public discussion on ethical technology will continue to play a strong role in future innovations and policymaking.

If we build strong, fundamental discourses in many places on something we know will have detrimental effects to society, it will permeate into stakeholders and state actors. They will know what to do, and civil society will know what to do.

I believe discourse and paradigm will shape the corridors of innovation.

For Powell, AI governance means enforcing existing laws, particularly those relating to data protection, anti-discrimination and human rights that apply to the institutional settings in which you put AI.

I would continue to advocate for thinking about institutional settings employing AI, rather than thinking about it as an object of regulation itself, she added.

The editorial views expressed in this article are solely those of the author and will not necessarily reflect the views of the GSMA, its Members or Associate Members.

Originally posted here:

Who is really responsible for ethical AI? - Mobile World Live

As platform engineering teams increasingly take on enterprise performance management tasks in production, there have been missed opportunities to give developers insights into their applications, experts say.

The instrumentation of application code is an area where platform engineers and SREs have stepped in on behalf of application developers, who aren't as steeped in the complexities of distributed cloud infrastructure systems such as Kubernetes. Analysts have also seen an increase in observability teams, specifically within the platform engineering discipline that connect developers' application performance insights with underlying infrastructure data.

"[There's] a move toward centralizing observability teams and centers of excellence," said Nancy Gohring, an analyst at IDC. "One driver for doing that is to try to control costs -- and one way those teams are trying to control costs is setting up data [storage] quotas for teams."

Such teams don't replace the need for developers to instrument their own application code but have helped ease the burden of managing the ongoing operational costs associated with collecting observability data, Gohring said.

There are some aspects of infrastructure monitoring, too, that developers don't need to concern themselves with, said Gregg Siegfried, an analyst at Gartner. Still, there remains a divide between the interests of platform teams in production observability and the interests of application developers, Siegfried said.

"I see an emergence of tools trying to give developers closer access to that data to give them more insight -- maybe allow them to put better instrumentation into the software," he said. "But none of them have really set the world on fire yet."

It's a commonly understood best practice in observability that developers instrument their own code before it's deployed to production, the better to manage its performance in the "you build it, you run it" mode of DevOps.

"I'm part of the OpenTelemetry End User Working Group. And recently we had somebody come in and talk to our user community about how they work in a company that really fosters an observability culture," said Adriana Villela, developer advocate at observability vendor LightStep, in a presentation at the recent Monitorama Conference. "The wonderful thing about it is that there is a directive from the executive saying, 'Thou shalt do observability and also developers are the ones instrumenting their own code,' which means that if you've got some disgruntled development team saying, 'I don't have time to instrument my code,' tough [s---]."

But some newer entrants to the market and their early customers question whether the devx, or developer experience, with observability needs to be quite so tough.

"Developers being able to add custom metrics to their code or spans or use observability tools is really critical to help developers take ownership of what they run in production," said Joseph Ruscio, a general partner at Heavybit, an early-stage investor in cloud infrastructure startups, in a Monitorama presentation.

However, to a new engineer, the overwhelming number of tools available for observability is "inscrutable and not at all welcoming to someone new to the craft," Ruscio said.

A production engineering team at a market research company is trying to make this task less onerous for developers using Groundcover's new Kubernetes-based APM tool. Groundcover uses eBPF to automatically gather data from Kubernetes clusters and associate it with specific applications, which could eventually replace the language-specific SDKs developers used to instrument applications using incumbent vendor Datadog.

"For what we are calling custom metrics that monitor a specific application's behavior, these will continue to be the responsibility of the developers," said Eli Yaacov, a production engineer at SimilarWeb, based in New York. "But we, the production engineers, can provide the developers the [rest of] the ecosystem. For example, if they are running Kubernetes, they don't need to worry about [instrumenting for] the default CPU or memory. Groundcover collects all this data in Kubernetes without requiring the developers to integrate with anything into their services."

Other emerging vendors also offer automated instrumentation features in debugging tools to instrument developers' apps without requiring code changes. These include Lightrun and Rookout.

Amid this year's general hype about generative AI, observability vendors have been quick to roll out natural language interfaces for their tools, mostly to add a user-friendly veneer over their relatively complex, often proprietary, data query languages. Such vendors include Honeycomb, Splunk, and most recently, Dynatrace and Datadog.

I see an emergence of tools trying to give developers closer access to that to that data to give them more insight -- maybe allow them to put better instrumentation into the software. Gregg SiegfriedAnalyst, Gartner

However, generative AI interfaces are not necessarily an obvious option to improve the developer experience of using observability tools, Siegfried said, as most developers are comfortable working in code.

"They have better things to do with their time than learn how to use an [application performance management] solution," he said.

Long term, generative AI and artificial general intelligence may have a significant effect, Ruscio said. But in the short term, Siegfried said he is skeptical that large language models such as ChatGPT will make a major impact on observability, particularly the developer experience.

Instead, unlike security and production-level systems monitoring, observability has yet to shift very far left in the development lifecycle, and developers would be best served by changing that, Ruscio said during his presentation. New and emerging vendors -- some of which are among Heavybit's portfolio companies -- are working in this area, termed observability-driven development.

"There's this missing mode where, wouldn't it be nice if you had some input when you are actually writing code as to what does this code look like in production?" Ruscio said. "It's cool that when I ship it, I'll get a graph. But why shouldn't I just know now, in my IDE, [how it will perform?]"

Beth Pariseau, senior news writer at TechTarget, is an award-winning veteran of IT journalism. She can be reached at [emailprotected] or on Twitter @PariseauTT.

Originally posted here:

IT pros mull observability tools, devx and generative AI - TechTarget