Applying
artificial intelligence for social good PART II
2.
AI capabilities that can be used for social
good
We identified 18 AI capabilities that
could be used to benefit society. Fourteen of them fall into three major
categories: computer vision, natural-language processing, and speech and audio
processing. The remaining four, which we treated as stand-alone capabilities, include
three AI capabilities: reinforcement learning, content generation, and
structured deep learning. We also included a category for analytics techniques.
When we subsequently mapped these
capabilities to domains (aggregating use cases) in a heat map, we found some
clear patterns (Exhibit 4).
Exhibit 4 SEE ORIGINAL ARTICLE
Image classification and object detection are powerful
computer-vision capabilities
Within computer vision, the specific
capabilities of image classification and object detection stand out for their
potential applications for social good. These capabilities are often used
together—for example, when drones need computer vision to navigate a complex
forest environment for search-and-rescue purposes. In this case, image
classification may be used to distinguish normal ground cover from footpaths,
thereby guiding the drone’s directional navigation, while object detection
helps circumvent obstacles such as trees.
Some of these use cases consist of tasks a
human being could potentially accomplish on an individual level, but the
required number of instances is so large that it exceeds human capacity (for
example, finding flooded or unusable roads across a large area after a
hurricane). In other cases, an AI system can be more accurate than humans,
often by processing more information (for example, the early identification of
plant diseases to prevent infection of the entire crop).
Computer-vision capabilities such as the
identification of people, face detection, and emotion recognition are relevant
only in select domains and use cases, including crisis response, security,
equality, and education, but where they are relevant, their impact is great. In
these use cases, the common theme is the need to identify individuals, most
easily accomplished through the analysis of images. An example of such a use
case would be taking advantage of face detection on surveillance footage to
detect the presence of intruders in a specific area. (Face detection
applications detect the presence of people in an image or video frame and
should not be confused with facial recognition, which is used to identify
individuals by their features.)
Natural-language processing
Some aspects of natural-language
processing, including sentiment analysis, language translation, and language
understanding, also stand out as applicable to a wide range of domains and use
cases. Natural-language processing is most useful in domains where information
is commonly stored in unstructured textual form, such as incident reports,
health records, newspaper articles, and SMS messages.
As with methods based on computer vision,
in some cases a human can probably perform a task with greater accuracy than a trained machine-learning model can. Nonetheless, the speed of “good enough” automated
systems can enable meaningful scale efficiencies—for example, providing
automated answers to questions that citizens may ask through email. In other
cases, especially those that require processing and analyzing vast amounts of
information quickly, AI models could outperform humans. An illustrative example
could include monitoring the outbreak of disease by analyzing tweets sent in
multiple local languages.
Some capabilities, or combination of
capabilities, can give the target population opportunities that would not
otherwise exist, especially for use cases that involve understanding the
natural environment through the interpretation of vision, sound, and speech. An
example is the use of AI to help educate children who are on the autism
spectrum. Although professional therapists have proved effective in creating
behavioral-learning plans for children
with autism spectrum disorder (ASD), waitlists for therapy are long. AI tools, primarily using emotion recognition and
face detection, can increase access to such educational opportunities by
providing cues to help children identify and ultimately learn facial
expressions among their family members and friends.
Structured deep learning also may have social-benefit
applications
A third category of AI capabilities with
social-good applications is structured deep learning to analyze
traditional tabular data sets. It can help solve problems ranging from tax
fraud (using tax-return data) to finding
otherwise hard to discover patterns of insights in electronic health records.
Structured deep learning (SDL) has been
gaining momentum in the commercial sector in recent years. We expect to see
that trend spill over into solutions for social-good use cases, particularly
given the abundance of tabular data in the public and social sectors. By
automating aspects of basic feature engineering, SDL solutions reduce the need
either for domain expertise or an innate understanding of the data and which
aspects of the data are important.
Advanced analytics can be a more time- and cost-effective
solution than AI for some use cases
Some of the use cases in our library are
better suited to traditional analytics techniques, which are easier to create,
than to AI. Moreover, for certain tasks, other analytical techniques can be
more suitable than deep learning. For example, in cases where there is a
premium on explainability, decision tree-based models can often be more easily
understood by humans. In Flint, Michigan, machine learning (sometimes referred
to as AI, although for this research we defined AI more narrowly as deep
learning) is being used to predict houses that may still have lead water pipes (Exhibit 5).
Exhibit 5 SEE ORIGINAL ARTICLE
3.
Overcoming bottlenecks, especially for data
and talent
While the social impact of AI is
potentially very large, certain bottlenecks must be overcome if even some of
that potential is to be realized. In all, we identified 18 potential
bottlenecks through interviews with social-domain experts and with AI researchers
and practitioners. We grouped these bottlenecks in four categories of
importance.
The most significant bottlenecks are data
accessibility, a shortage of talent to develop AI solutions, and “last-mile”
implementation challenges (Exhibit 6).
Exhibit 6 SEE ORIGINAL ARTICLE
Data needed for social-impact uses may not be easily accessible
Data accessibility remains a significant
challenge. Resolving it will require a willingness, by both private- and
public-sector organizations, to make data available. Much of the data essential
or useful for social-good applications are in private hands or in public
institutions that might not be willing to share their data. These data owners
include telecommunications and satellite companies; social-media platforms;
financial institutions (for details such as credit histories); hospitals,
doctors, and other health providers (medical information); and governments
(including tax information for private individuals). Social entrepreneurs and
nongovernmental organizations (NGOs) may have difficulty accessing these data
sets because of regulations on data use, privacy concerns, and bureaucratic
inertia. The data may also have business value and could be commercially
available for purchase. Given the challenges of distinguishing between social
use and commercial use, the price may be too high for NGOs and others wanting
to deploy the data for societal benefits.
The expert AI talent needed to develop and train AI models is in
short supply
Just over half of the use cases in our
library can leverage solutions created by people with less AI experience. The
remaining use cases are more complex as a result of a combination of factors,
which vary with the specific case. These need high-level AI expertise—people
who may have PhDs or considerable experience with the technologies. Such people
are in short supply.
For the first use cases requiring less AI
expertise, the needed solution builders are data scientists or software
developers with AI experience but not necessarily high-level expertise. Most of
these use cases are less complex models that rely on single modes of data
input.
The complexity of problems increases
significantly when use cases require several AI capabilities to work together
cohesively, as well as multiple different data-type inputs. Progress in
developing solutions for these cases will thus require high-level talent, for
which demand far outstrips supply and competition is fierce.
‘Last-mile’ implementation challenges are also a significant
bottleneck for AI deployment for social good
Even when high-level AI expertise is not
required, NGOs and other social-sector organizations can face technical
problems, over time, deploying and sustaining AI models that require continued
access to some level of AI-related skills. The talent required could range from
engineers who can maintain or improve the models to data scientists who can
extract meaningful output from them. Handoffs fail when providers of solutions
implement them and then disappear without ensuring that a sustainable plan is
in place.
Organizations may also have difficulty
interpreting the results of an AI model. Even if a model achieves a desired
level of accuracy on test data, new or unanticipated failure cases often appear
in real-life scenarios. An understanding of how the solution works may require a data scientist or “translator.” Without one, the NGO or other implementing
organization may trust the model’s results too much: most AI models cannot
perform accurately all the time, and many are described as “brittle” (that is,
they fail when their inputs stray in specific ways from the data sets on which
they were trained).
By Michael Chui, Martin Harrysson, James Manyika, Roger Roberts, Rita Chung, Pieter Nel, and Ashley van Heteren
https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good?cid=other-eml-alt-mgi-mck-oth-1811&hlkid=485b551e30be4defadc2592045042ed6&hctky=1627601&hdpid=465daed6-1d79-427b-9ff3-417da5844a17
CONTINUES IN PART III
No comments:
Post a Comment