Ensuring the ethical use of AI-generated content, especially in contexts like deepfakes and misinformation, involves several strategies and considerations:

1. Transparency: Clearly label AI-generated content. Users should be able to easily distinguish between content created by humans and content generated by AI. This helps in setting the right expectations and understanding the origin of the information.
2. Consent and Privacy: Obtain consent from individuals whose likeness (e.g., voice, image) is used to create AI-generated content. This is crucial in preventing unauthorized use of personal attributes, especially in sensitive or personal contexts.
3. Regulations and Guidelines: Adhere to legal and regulatory standards governing the use of AI. Many jurisdictions are considering or have implemented regulations that address the creation and dissemination of AI-generated content, including deepfakes.
4. Ethical AI Practices: Implement and follow ethical guidelines for AI development and deployment. This includes ensuring that AI systems are fair, non-discriminatory, and do not propagate biases. Organizations like the IEEE, ACM, and others provide frameworks and guidelines for ethical AI.
5. Verification Tools: Use or develop tools that can detect AI-generated content. These tools can help platforms and end-users identify manipulated content before it spreads, thus mitigating potential harm.
6. Education and Awareness: Educate users about the capabilities and risks associated with AI-generated content. Understanding how AI works and recognizing its potential misuse can empower users to critically assess the content they consume.
7. Content Provenance: Implement digital provenance tools that track and verify the source of digital content. This can help establish the authenticity of content circulating online.
8. Industry Collaboration: Collaborate across the tech industry to develop standards and best practices for responsibly creating and sharing AI-generated content. This includes sharing knowledge about threats and defense mechanisms.
9. Continuous Monitoring: Regularly review the impact of AI-generated content and adapt policies as necessary. This dynamic approach can respond to evolving technologies and misuse patterns.

These measures, collectively, can help mitigate risks associated with AI-generated content and encourage its use in a manner that is ethical, responsible, and aligned with societal values.

The post How can we ethically manage AI-generated content to prevent deepfakes and misinformation? appeared first on Artificial Intelligence.

In March 2021, Google announced that it was ending support for third-party cookies, and moving to “a more privacy first web.” Even though the move was expected within the industry and by academics, there is still confusion about the new model, and cynicism about whether it truly constitutes the kind of revolution in online privacy that Google claims.

To assess this, we need to understand this new model and what is changing. The current advertising technology (adtech) approach is one in which platform corporations give us a “free” service in exchange for our data. The data is collected via third-party cookies downloaded to our devices, that allow a browser to record our internet activity. This is used to create profiles and predict our susceptibility to specific ad campaigns.

Recent advances have allowed digital advertisers to use deep learning, a form of artificial intelligence (AI) wherein humans do not set the parameters. Although more powerful, this is still consistent with the old model, relying on collecting and storing our data to train models and make predictions. Google’s plans go further still.

Patents and plans

All corporations have their secret sauce, and Google is more secretive than most. However, patents can reveal some of what they’re up to. After an exploration of Google patents, we found U.S. patent US10885549B1, “Targeted advertising using temporal analysis of user-specific data”: a patent for a system that predicts the effectiveness of ads based on a user’s “temporal data,” snapshots of what a user is doing at a specific point instead of indiscriminate mass data collection over a longer time period.

We can also make inferences by examining work from other organizations. Research funded by adtech company Bidtellect demonstrated that long-term historical user data is not necessary to generate accurate predictions. They used deep learning to model users’ interests from temporal data.

Alongside contextual advertising — which displays ads based on the content of the website on which they appear — this could lead to more privacy-conscious advertising. And without storing personally identifiable information, this approach would be compliant with progressive laws like the European Union’s General Data Protection Regulation (GDPR).

Google has also released some information through the Google Privacy Sandbox (GPS), a set of public proposals to restructure adtech. At its core are Federated Learning Cohorts (FLoCs), a decentralized AI system deployed by the latest browsers. As the Google AI blog explains, federated learning differs from traditional machine learning techniques that collect and process data centrally. Instead, a deep learning model is downloaded temporarily onto a device, where it trains on our data, before returning to the server as an updated model to be combined with others.

With FLoCs, the deep learning model will be downloaded to Google Chrome browsers, and analyze local browser data. It then sorts the user into a “cohort,” a group of a few thousand users sharing a set of traits identified by the model. It makes an encrypted copy of itself, deletes the original and sends the encrypted copy back to Google, leaving behind only a cohort number. Since each cohort contains thousands of users, Google maintains that the individual becomes virtually unidentifiable.

Cohorts and concerns

In this new model, advertisers don’t select individual characteristics to target, but instead advertise to a given cohort, as Google’s Github page explains. Although FLoCs may sound less effective than collecting our individual data, Google claims they realize “95 per cent of the conversions per dollar spent when compared with cookie-based advertising.”

The bidding process for ads will also take place on the browser, using another system codenamed “Turtledove.” Soon, Google adtech will all work this way, contained on a web browser, making constant ad predictions based on our most recent actions, without collecting or storing personally identifiable information.

We see three key concerns. First, this is only part of a much larger AI picture Google is building across the internet. Through Google Analytics, for example, Google continues to use data gained from individual website-based first-person cookies to train machine learning models and potentially build individual profiles.

Secondly, does it matter how an organization comes to “know” us? Or is it the fact that it knows? Google is giving us back legally acceptable individual data privacy, however it is intensifying its ability to know us and commodify our online activity. Is privacy the right to control our individual data, or for the essence of ourselves to remain unknown without consent?

The final issue concerns AI. The limitations, biases and injustice around AI are now a matter of widespread debate. We need to understand how deep learning tools in FLoCs group us into cohorts, attribute qualities to cohorts and what those qualities represent. Otherwise, like every previous marketing system, FLoCs could further entrench socio-economic inequalities and divisions.

The post Google’s AI advertising revolution: More privacy, but problems remain appeared first on Artificial Intelligence.

Few of us, if any, read user agreements prior to entering personal information or allowing applications to track our location. This is incredibly convenient for data corporations. The legal requirements for your explicit consent are camouflaged in microscopic print that seems to go on for pages and pages. As such, we are basically signing away our privacy rights, all to access the latest trend in smartphone applications.

A recent New York Times op-ed outlined how location tracking services embedded in smartphone apps are being archived and disseminated by data corporations. The thought of this occurring without users’ knowledge is both unsettling and enigmatic. An investigation led to the revelation that millions of Americans are having their locations tracked through their smartphones. And since the practice is legal in the U.S., the applied uses of location data are endless. Although the article is focused on the U.S. population, the same deceptive methods are carried out in Canada, as well. 

A 2018 report by The CBC names major Canadian telecoms complicit in the mining and selling of users’ location data. Any time you allow an app to use your location data, you are essentially “consenting” to having your location data collected and stored. Even if you don’t want your data distributed, once consent is given, these companies are legally permitted to obtain user locations — among other personal information — and sell that information to third parties.  

Although the laws in Canada and the U.S. demand different levels of oversight, the telecommunication companies operating in Canada are doing just enough to remain barely legal. The Office of the Privacy Commissioner of Canada says that “meaningful consent” must be obtained prior to gathering personal data. However, even if individuals do consent, are they aware of what they are consenting to?

Some of us here at SFU might be indifferent about this issue, saying, “Who cares if our locations are being perpetually tracked?” and “who cares what companies have access to this information?” It seems that we care about consent only when it is convenient, or when the potential for misuse is directly perceptible in our everyday lives. 

These data corporations are betting on us being too lazy to investigate exactly what we are consenting to. However, our growing reliance on technology and the rate at which it is being developed and distributed continues to blur the lines of cyber ethics. This makes government oversight over these companies increasingly difficult to legislate and enforce, thus delegating the task of trying to interpret “meaningful consent” onto the user.   

Aside from reading the entirety of the user agreement — which I don’t expect anyone is going to do — there are a few relatively easy ways of lessening the likelihood that your location will be tracked. Start by limiting the number of applications that operate using location services. Turn off location services for programs that are still able to operate without them. 

Even taking these precautions, there is evidence that your location is still tracked, notwithstanding users’ explicit instructions not to. There’s no reason why we should make this any easier on data miners. Educate yourself on what your phone is doing behind the scenes and protect yourself — and your location — from prying eyes.

The post We have to pay better attention to who and what has access to our data appeared first on Artificial Intelligence.