The arrival of frontier technologies such as Space 2.0 systems – along with other innovations including artificial intelligence (AI), 5G, the Internet of Things (IoT) and robotics – offers further potential for supporting the fight against climate change.

A new tool is promising to make your pictures undetectable to facial recognition software without significantly changing their appearance. Known as Photo Ninja, the tool uses artificial intelligence (AI) to make minor alterations to photos that reportedly confuse facial recognition algorithms. Developed by DoNotPay, the company behind the app that uses AI to provide legal services, Photo Ninja moves the location of certain pixels, alters colors, and adds unnoticeable objects into images. "Photo Ninja uses a novel series of steganography, detection perturbation, visible overlay, and several other AI-based enhancement processes to shield your images from reverse image searches without compromising the look of your photo," the company said. CEO Joshua Browder revealed on Twitter Monday that Photo Ninja will be integrated into the DoNotPay app for paying customers.

The FTC has issued Business Guidance about the use of artificial intelligence (AI), warning marketers about the danger of the potential discriminatory impact of automated decision-making. As the FTC noted, the use of AI "presents risks, such as the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities." To illustrate the risk, the FTC's Guidance cites a study of an algorithm used to target medical interventions to the sickest patients that wound up funneling resources to a healthier, white population, to the detriment of sicker, black patients. The Guidance also cites a number of enforcement actions brought by the FTC and other government agencies under the Fair Credit Reporting Act, the Equal Credit Reporting Act, the Fair Housing Act, and more, where companies have used big data in misleading or discriminatory ways in their advertising, targeting practices or in their other interactions with consumers. To mitigate these risks, the FTC's Guidance recommends that companies using artificial intelligence tools be "transparent, explainable, fair, and empirically sound, while fostering accountability."

The speed at which banking regulations and auditing policies are changing is unprecedented. AI and machine learning (ML) are emerging as reliable and effective tools for both fintech and traditional banks to maintain regulatory compliance and safeguard their customers' information. During this digital transformation, it is important to recognize the increasing complexity that fintech and banks are encountering when it comes to customer experience and retention. Keeping up with federal and local regulatory changes in a timely manner is mission critical. This white paper will provide a brief overview of how fintech and banks can gain a competitive edge using AI and ML applications.

In 2021, our world is driven by data. As the push for ESG measurement and disclosure grows, this truism is extending into the space as never before. From radical innovations in tech to systems change and social impact, several key ideas are emerging to make sense of collecting, managing and reporting ESG data. Below are the top trends, as identified by speakers at GreenBiz Group's GreenFin 21. As the ESG ecosystem expands worldwide, demands for ESG data collection, analysis and disclosure are growing in number and depth.

The European Union has published a new framework to regulate the use of artificial intelligence across the bloc's 27 member states. The proposal, which will take years to implement into law and will be subject to many tweaks and amendments during this time, nevertheless constitutes the most ambitious AI regulations seen globally to date. The regulations cover a wide range of applications, from software in self-driving cars to algorithms used to vet job candidates, and arrive at a time when countries around the world are struggling with the ethical ramifications of artificial intelligence. Similar to the EU's data privacy law, GDPR, the regulation gives the bloc the ability to fine companies that infringe its rules up to 6 percent of their global revenues, though such punishments are extremely rare. "It is a landmark proposal of this Commission. It's our first ever legal framework on artificial intelligence," said European Commissioner Margrethe Vestager during a press conference.

MOST LAWS are local--except in the digital realm. When the European Union comes up with some new tech regulation, it can quickly spread around the world. Global companies adopt its typically strict rules for all their products and markets in order to avoid having to comply with multiple regimes. Other governments take more than one page from the EU's rule book to help local firms compete. The textbook example for what has been dubbed the "Brussels effect", is the EU's General Data Protection Regulation (GDPR), which went into force in 2018 and swiftly became the global standard. Your browser does not support the audio element.

As a patent attorney with 22 years of experience, I've observed transformations across dozens of industries by research and analysis of patent data to identify patterns of technology shifts. One of those technology shifts that will change the way data is collected, processed, analyzed, and communicated is edge computing. While the cloud is great at what it does – storing massive amounts of data, documents, photos, music, videos, games, etc. and delivering it as requested through the Internet worldwide. For today's needs, it mostly works well. But the world is changing continuously and more quickly than ever, moving from millions or billions of connected things to more than one trillion connected things as sensors are deployed for autonomous vehicles, traffic management, and mobile devices in use everywhere.

The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value--what progress and direction does a field promote, optimization--how the defined system within a problem formulation relates to broader dynamics, consensus--how agreement is achieved and who is included in building it, and failure--what methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.

Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. However, how do we ensure trust in these systems? Regulation changes such as the GDPR mean that users have a right to understand how their data has been processed as well as saved. Therefore if, for example, you are denied a loan you have the right to ask why. This can be hard if the method for working this out uses "black box" machine learning techniques such as neural networks. TrustyAI is a new initiative which looks into explainable artificial intelligence (XAI) solutions to address trustworthiness in ML as well as decision services landscapes. In this paper we will look at how TrustyAI can support trust in decision services and predictive models. We investigate techniques such as LIME, SHAP and counterfactuals, benchmarking both LIME and counterfactual techniques against existing implementations. We also look into an extended version of SHAP, which supports background data selection to be evaluated based on quantitative data and allows for error bounds.