According to Oxford’s Government AI Readiness Index 2020, the Philippines ranked 74th out of 172 countries. The country also ranked 9th out of 15 countries at the regional level. In the 2019 report, the Philippines ranked 50th out of 174 countries.

With an overall index score of 42.94 out of 100, the Philippines scored the highest in terms of data availability at 70.92 but received a low mark in governance and ethics at 52.18.

The Department of Trade and Industry previously said it is eyeing to implement the Philippine AI roadmap this year in a bid to make the country an “AI center of excellence” in the region.

The said roadmap covers the implementation of AI in select industries such as agribusiness, manufacturing, and services sectors.

Meanwhile, some businesses, especially those in the financial sector, are already at the forefront of using chatbots and other AI technologies to keep up with surging customer demand and reduce transactions that needed human contact.

Dr. Cheng cautioned that without any way to ensure ethical AI, the data used to train intelligent systems used by organizations may be flawed and may cause widespread harm.

“If we feed them (intelligent systems) data that is biased, it will also be biased. So it will be bias in, bias out. If the data that we give them is discriminatory, discrimination in, discrimination out. And the problem with these systems is that it’s automated, it’s easy to deploy,” she added.

As an example, Dr. Cheng said a company could be using AI to speed up the selection of candidates. The data used to train the system, however, could lead to the AI having unfair preference over certain candidates.

Amazon encountered this dilemma years ago when it attempted to use an AI tool to recruit talents. The system ended up having a preference for male candidates after it was fed with resumes the company received over the years.

“Are the preference and non-preference justifiable? Are they correct? And can we even identify the preferences of this system? There are systems being built and people cannot even explain how it is doing its task,” Dr. Cheng added.

“If it’s working blindly if there’s some failure at some point [and] somebody sued, who’s going to be sued? You cannot sue a software, right? So it’s the company that will take in the liability and how is that going to be explained?”

Moderation is key

However, Dr. Cheng also cautioned that too much regulation will be counterintuitive as it might stifle progress in the field, especially when it comes to research.

“There must be some regulation but I don’t want it to be stifling, I don’t want the field to be highly regulated. Too much regulations might deter development,” she added.

As an added level of protection, Dr. Cheng also said that developers should exercise self-regulation as there are already published guiding principles that can serve as a template for AI practitioners.

“Before we build, we have to consider already, the possible effect of the system on behalf of those who would be affected,” she added.

“In the end, it is us, humans, we should be the ones who should be reflective, who should be careful, and who should be cautious of the data that we use and the applications that we are building that will influence the output of these artificially intelligent systems.”